i

A Network Approach to Web 2.0 Social Influence: The Influentials, Word-of-Mouth

(WOM) Effect, and the Emergence of Social Network on Facebook

by

Kyounghee Kwon

December 28, 2010

A dissertation submitted to the

Faculty of the Graduate School of

The University at Buffalo, State University of New York

in partial fulfillment of the requirements for the

degree of

Doctor of Philosophy

Department of Communication

UMI Number: 3440305

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UMI 3440305

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ii

ACKNOWLEDGEMENTS

Dr. George Barnett, my beloved advisor who has trusted in my potential for five years! I

cannot say thank you enough.

Dr. Mike Stefanone, the best mentor, colleague, and friend. Cheerio!

Dr. Frank Tutzauer, the great teacher who guided me to a different way of looking at

communication process. I do want to get into math a bit more.

Dr. Sang-Gil Lee and Dr. Tae-Jin Yoon, you touch my heart and mind always. I just miss

old days I had spent with you at Sung-Ahm Kwan.

Parents and my sisters Sunwha and Mikyung, I could have not come this far without your

support. In my prayer, you are always with me.

One anonymous friend, you take my side unconditionally. You are a gift.

Sewhan oppa, thank you for putting up with me for all the years we’ve spent together. I’ll

be better for you.

God. Thank you. I am nothing without you.

(Note: This dissertation project was supported by Mark Diamond Research Fund.)

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TABLE OF CONTENTS

Acknowledgements ...........................................................................................................iii List of Tables .....................................................................................................................iv List of Figures ....................................................................................................................v Abstract .............................................................................................................................vi I.INTRODUCTION............................................................................................................1 II. THEORETICAL BACKGROUND …………………………….................................12 III. SNS ENRICHES PERSONAL NETWORK ANALYSIS..........................................21 IV. PROJECT DESCRIPTION…………….....................................................................28 V. THE FACEBOOK INFLUENTIALS: ON THEIR SOCIAL NETWORK CHARACTERISTICS.......................................................................................................39 VI. WORD-OF-MOUTH ON FACEBOOK: STRUCTURAL APPROACH......................................................................................................................72 VII. EMERGENT GROUP STRUCTURES ON FACEBOOK: SCALE-FREE, SMALL WORLD, AND NETWORK ENTRALIZATION..........................................................110 VIII. CONCLUSION AND DISCUSSIONS….………….............................................144 REFERENCES…………………………………..……………………………………. 153 APPENDIX …………………………………………………………………………….176

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LIST OF TABLES

Table 1. Terminology Summary …...………....................................................................35

Table 2. Curve Estimation for Group Evolution................................................................38

Table 3. Personality Strength Index Items……….…………............................................53

Table 4. King and Summers’ Opinion Leadership Scale..................................................55

Table 5. Factor Analysis of Facebook Social Attributes...................................................57

Table 6. Means, Standard Deviations and Correlations of Variables…............................64

Table 7. Multiple Regression Analysis for Predicting Self-Perceived Opinion Leadership Measured by King and Summers’ Index. .........................................................................65

Table 8. Multiple Regression Analysis for Predicting Observed Opinion Leadership Measured by Behavioral Influence Outcome....................................................................67

Table 9. Correlations between Network Variables and Opinion Leadership....................69

Table 10. Mean, Standard Deviations, and Correlations of IVs......................................100

Table 11. GEE Models Predicting Contagion Effect on Invitees’ Support for the Advocacy Group .............................................................................................................104

Table 12. The Effects of Direct Contact and PNE at Three Different Levels of Embeddedness……………………………………………………………......................105

Table 13. Descriptive Statistics of 67 Componets...........................................................133

Table 14. Small-world Effect on Network Recruitment………………………………..135

Table 15. Scale-Free Structure of Strategically Emerged and Generic Social Networks on

Facebook……………………………………………………………………………….137

Table 16. Small-World Network in Facebook: A Comparison………………………...138

Table 17. Degree and Betweenness Centralization: A Comparison…………………...140

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LIST OF FIGURES

Figure 1. Layers of Personal Network…………….......................................................... 26

Figure 2. The Snapshots of the Advocacy Group Emerged from the Project................... 32

Figure 3. Over Time Increase of Group Membership, from January 25th to January 30st,

2010……………………………………………............................................................... 36

Figure 4. Plots of R- and S-Curve………………............................................................. 39

Figure 5. Ego-networks of Two Recruited Inviters, with the Same Size but Different Density………………………………………………………………………………….. 59

Figure 6. An Example of Personal Network Exposure to Social Information….............. 81

Figure 7. Simple Representation of Three Social Contagion Mechanisms……………...84

Figure 8. Social Structures of Structural Equivalence and Cohesion………………........87

Figure 9. Presumed and Real Structure of Facebook Personal Networks……………….96

Figure 10. Centralization Comparison between Two Scale-Free Networks…………....125

Figure 11. Visualization of Network Formation: Real versus Theoretical Networks.....127

Figure 12. Degree Distribution of Members in the Advocacy Group………………….132

Figure 13. Log-log Plot to Test Scale-Free Network……………………………….......132

vi

ABSTRACT

Social Network Sites (SNS), the most prevalent Web 2.0 service, blossom with

interpersonal sharing practices. A culture of sharing and the subsequent production of

abundant social information are driven by the visibility of digital networked-ness. Social

information embedded in digital social networks influences the shaping of our attitudes,

thoughts, and online behaviors. Marketers and campaigners have been taking advantage

of SNS’s social information for various instrumental goals. Based on a cyber-field

behavioral experiment on Facebook, this dissertation attempts to theorize underlying

mechanisms of the SNS social influence process and analyze the ensuing formation of a

collective communication structure. This dissertation particularly emphasizes the social

network effects on the influence outcome. Multilevel-perspectives are employed to

analyze the dynamics of Facebook’s social network influence.

The findings are summarized as follows. First, an individual-level examination

was performed. Particularly, personal influence on mobilizing others’ online behaviors

was explored. The results found that the influentials in SNS were characterized as not

only having a leader-like personality, but also being digitally connected to a diverse and

large number of social contacts through participation in multiple virtual group activities

and maintenance of heterogeneous personal networks. The findings of this project

support the concept that the personal influence on Facebook should be understood as

normative rather than informational.

Second, structural social influence has been theorized based on three sub-

mechanisms: Direct recommendation, social contagion, and network embeddedness. The

study found the following: (a) message compliance was stronger when individuals were

vii

exposed to multiple direct contacts; (b) Facebook displayed the effect of indirect

exposure to others’ behaviors, not just the effect of direct recommendation; in other

words, it has a social contagion effect; and (c) While network embeddedness did not

directly affect an individual’s online behavior, this positional property acted as a

moderator for direct recommendation and social contagion effects.

Lastly, a macro-structural analysis was conducted to explore the properties of

communication systems that emerged through social influence processing. The

community structure strategically formed by this field experiment was examined based

on the three well-known network topologies –scale-free, small-world, and centralization,

finding the the emerged community was characterized as scale-free and small-world like

and weakly centralized. The implications of having such structural properties on the

effectiveness of communication system are discussed.

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I. INTRODUCTION

1. 1. Background

More than half a decade has passed by since the term “Web 2.0” was first

introduced to the public. O’Reilly and his colleagues, who popularized the term,

proposed a new vision of online culture during the first Web 2.0 conference they hosted

in 2004. According to O’Reilly (2005), Web 2.0 is characterized as a collection of Web

applications that leverage the “long-tail” composed of ordinary users’ participation, self-

service, sharing and collaboration. While some computer engineers including Tim

Berners-Lee, the inventor of the Web, criticized the term as a “piece of jargon” irrelevant

with technological innovation, the majority of users have been nevertheless experiencing

the popularization of sharing culture online.

Social Network Sites (SNS) are a prominent example of Web 2.0 services. The

sharing and participatory culture encompassed by SNS enriches networking practices

online. Although the Web is inherently a network of networks from its inception, recent

SNS substantiates the networked nature through end-users’ socio-cultural practices. In

SNS, users communication networks not only exist as an infrastructure under the surface

but also take part of the visible and utilizable content areas.

Online social networks brim with social information. On Facebook, for example, I

can easily check out my friend’s profile to see how he or she feels today; I know what my

friend watched on Youtube last night through the shared hyperlink; I receive a

recommendation from a friend for what he or she supports; and even when my friend did

not directly recommend it to me, a computer-automated advertisement would tell me who,

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among my friends, are using a particular product or engaging in a certain action. Just with

Facebook alone, the examples of social information can go on and on. Thus, social

information is the outgrowth from the mixture of the technology that actualizes the

networked nature of the Web and the users’ willingness to participate in social

networking activities.

The abundance of social networking and the subsequent production of social

information are interesting characteristics because they lead scholars to reconsider the

conventional computer-mediated communication (CMC) literature that focuses on users’

intra-psychological processes. The CMC literature has discussed communication

conditions unique to technologically mediated contexts and the subsequent effects on

social psychology. Particularly, the issue of anonymity is often highlighted in CMC

scholarship (e.g., Etzioni & Etzioni, 1999; McKenna & Bargh, 2000; Postmes, Spears,

Sakhel, & de Groot, 2001; Turkle, 1995). Some studies have attributed anti-social online

behaviors to anonymity (Davis, 2002; Suler & Philips, 1998). Others have shown that it

may foster a de-individuation process in group communication contexts (Jessup,

Connolly, & Galegher, 1990; Postmes & Spears, 2000). In contrast, communication

online is often characterized as "hyperpersonal," referring to the process of perceiving a

communication partner more favorably than offline due in part to anonymity (Nowak,

Watt, & Walther, 2005; Walther, 1996). Walther (1996) proposes that the hyperpersonal

effect occurs as a part of the social information processing of CMC, arguing that

anonymity can be ultimately overcome in developing interpersonal relationships in CMC

as interactions are accumulated over time between communication partners. Regardless

of which aspect is supported, these arguments are based on the common ground that

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anonymity and the lack of social cues are a part of the inherent nature of CMC that is

worthy of distinctive attention in contrast to face-to-face context.

To what extent shall this common sense be applicable to CMC in SNS? Many

activities emerging in SNS actually depend on the interconnectedness among already

acquainted and thus non-anonymous social ties, widely ranging from the intimate to the

extremely superficial. It is hard to simply say that SNS lack social cues, either. Rather, a

different level of social cues are hinted at from personal portfolios that accumulate

pictures, gifts, status updates, thoughts, activities, preferences, friends’ information,

social relational states, and many other kinds of social information. On Facebook, in

particular, the richness of social information is evident from its own statistics that more

than three billion photos and five billion pieces of content are uploaded each month

(Facebook, 2010). Along with the reduced anonymity and the presence of physical and

various social cues, the CMC theories need to expand their coverage beyond intra-

psychological processes. Talking about hyperpersonal effect or de-individuation

processes with the negligence of social contextual effect may not be as insightful with

SNS as with more traditional modes of CMC.

This dissertation underscores that the use of preexisting CMC theories that put

heavy weight on anonymity or social cues is not enough to capture the newly emerging

interpersonal practices in Web 2.0 context, especially in SNS. To understand impacts of

social information produced through the Web 2.0 practices on users’ behaviors and

attitudes, instead, we can welcome a newer theoretical lens which sheds light on the most

essential nature of online relationships, the networked-ness.

4

Some online behaviors are based in the pursuit of self-interest, such as online

shopping or online banking. These actions are instrumental, purposive and goal-oriented.

When engaging in these actions, users are mindful of the consequences of the action at

varying levels. Some other behaviors, on the other hand, are expressly done for the

purpose of communication itself. Examples of these behaviors include leaving greeting

messages on a friend’s profile or exchanging emotional support messages in a support

group. Meanwhile, there are other behaviors neither purely instrumental nor completely

expressive. Back to the Facebook example, I may click a hyperlink without any well-

defined purpose, simply because it is followed by many “likes”. I may sign an online

petition as a response to a friend’s recommendation despite the fact I am quite indifferent

to the issue at hand. Also, I may become a fan of what my friends already are, not

necessarily because being a fan would be a mode expressive communication toward my

friends, but simply because that thing (or whatever it may be) looks cool. In these

examples, self-interest or expressiveness partially define the actions yet are not the

primary motivation.

This dissertation is based on the idea that these “neither instrumental nor

expressive” behaviors constitute a distinctive regime of online behaviors that are worthy

of scholarly attention. These behaviors are not mutually exclusive from instrumental or

expressive actions. Nevertheless, I observe one outstanding characteristic of such actions

that is distinctive from typical instrumental or expressive actions: The action is enacted

only when an actor receives the information about others’ behaviors or attitudes. The

information originates from the actor’s online social networks, whether it is in the form

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of a straightforward request, recommendation or suggestion, or through the actor’s

unintended exposure to others who already engaged in the action.

In SNS, socially influenced behaviors are not just popular phenomena. Social

influence is a fundamental mechanism underlying more complex word-of-mouth (WOM)

effects that are aggressively utilized in online marketing and campaigning (e.g. Lescovec,

Adamic, & Huberman, 2007; Subramani & Rajagopalan, 2003). Chapters 5 and 6 in this

dissertation particularly highlight social influence as a structural mechanism of WOM

processes and discuss the relevant processes and consequences on the micro and macro

levels.

Noting the prevalence and significance of behaviors responsive to social

information, this dissertation explores the dynamics of interpersonal influence occurring

in SNS. The dissertation covers multi-level perspectives, including the message sender’s

personal influence, social network effects, and the macro structure of emergent strategic

networks. As Monge and Contractor (2003) suggest, the multilevel approach enriches

discussions on the emergence of communication networks. The multilevel approach

accompanies a variety of theoretical frameworks, given that the explanation about

organizational behaviors needs to be multi-level ranging from the individual’s traits to the

group as a whole. Monge and Contractor (2003) particularly propose statistical network

analysis techniques, called p*, as an integrated analysis tool. P* is used when the purpose

of the study is to see whether the macro-level of network properties are attributed to the

formation of networks even with the lower-level of properties put simultaneously in

consideration. Although the research questions posited in this dissertation are not

answerable by p*, the dissertation aligns with Monge and Contractor’s (2003) essential

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idea that communication networks should be researched with a multi-level perspective.

Different levels of analysis are introduced in each of the following chapters.

2. 2. Overview of the Chapters

Chapter Two

Prior to reviewing the empirical analysis on SNS interpersonal influence, the

second chapter lays the theoretical background. The empirical exploration of this

dissertation is based on a cyber-behavioral experiment with the case of the formation of a

campus advocacy network on Facebook, which was actually initiated by the several

members of the student government at the University at Buffalo in Fall 2009.

The study was conducted on Facebook, a popular SNS (www.facebook.com). The

reputation of Facebook as a social networking media is already widely documented by

many researchers (e.g. Ellison, Steinfield & Lampe, 2007; Walther, et al., 2009; Wang,

Moon, Kwon, Evans & Stefanone, 2010). In this chapter, I invite personal and social

influence literature as important theoretical frameworks to understand the Facebook

social phenomenon.

Specifically, I review two approaches of influence that have been widely cited by

existing interpersonal influence literature in the Communication field. In the first

approach, interpersonal influence is conceptualized as personal influence. In this tradition,

the focus of inquiry is to define the influentials based on their personal and social traits. I

draw upon opinion leadership literature for this part (e.g. Katz & Lazarsfeld, 1955;

Weimann, 1994). Defining characteristics and roles of opinion leadership have been an

important topic in strategic communication such as marketing and campaign research.

Given that the advocacy is also a strategic communication, it is worthwhile to investigate

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how to characterize opinion leaders in SNS and what kind of impact they have on their

peers.

The other approach to study influence is to understand influence as the process of

learning or imitating through observing the social environment. In SNS, abundant social

information provides online actors with more chances to observe networked others’

behaviors than any other traditional CMC context. Particularly, I take a structural

approach to explore how individuals’ positional properties within social networks

produce the normative influence they receive (e.g. Feeley & Barnett, 1997; Meyer, 1994).

Social network analysis is a method dedicated to the structural analysis of social

relationships (Barnett, Danowski, & Richards, 1993). In the tradition of social network

analysis, social influence has been a major topic of interest. In this chapter, I will review

the network theories of social contagion (Burt, 1987; Marsden & Friedkin, 1993) that

particularly highlight the influence of imitation or learning from the social context in

which an actor is located.

After the review, I discuss why both personal influence and the contagion effect

need to be considered in studying social influence in a SNS context. To do so, I

conceptualize interpersonal influence occurring in SNS as a dialectic outcome between

compliance (Cialdini & Trost, 1998; Kelman, 1958) on a dyadic level and structural

social influence on a network level (Fulk, Steinfield, Schmitz, & Power, 1987; Rice &

Ayden, 1991; Salancik & Pfeffer, 1978). The effect of opinion leadership is

conceptualized as a part of the compliance process, while the effect of social information

is understood as contingent on the structural network effect.

Chapter Three

8

The third chapter addresses methodological aspects of the project. In

Communication, the contemporary literature on social influence shows two weaknesses:

First, although actual attitude or behavioral change must be the most explicit outcome of

social influence, most influence literature is based on self-perception or self-evaluation

rather than actual observation of change. Operationalizing interpersonal influence based

on self-perception can be problematic due to the inherent potential of “disconfirmed

social projection,” the false assumption that a respondent accurately estimates others’

attitudes or behaviors (Gerard & Orive, 1987). Rice and Mitchell (1973) and Rice and

Aydin (1991) confirmed the existence of disconfirmed social projection, finding no

significant correlation between the subjects’ estimation of others’ attitudes and the others’

actual reports on their attitudes.

The second weakness is that contemporary influence studies treat social influence

as if it were an “intraindividual” psychological process. This tendency disregards the fact

that people form or maintain their attitudes or behaviors not in isolation but in interwoven

social contexts (Visser & Mirabile, 2004). While the early influence researchers

arduously devoted themselves to uncovering social structural and positional impacts on

an individual’s attitude formation (e.g. Cartwright & Harary, 1956; Festinger, 1954;

Festinger, Schachter, & Back, 1950; Heider, 1946), a recent trend of influence research

reveals the predominant orientation toward intraindividual attributes over social context.

As Eagly and Chaiken (1993) and Visser and Mirabile (2004) criticize, the insufficient

attention to the social structural dimension could result in“serious omissions and

limitations” to understanding the mechanisms of social influence (Eagly & Chaiken, 1993,

p. 682).

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The communication field is not exempt from their criticism. Communication

research on interpersonal and social influence has been related to persuasion studies,

most of which put emphasis on the one-to-one communicative situation. Therefore, the

nature of embeddedness of an actor within a larger relational structure might have not

been of much interest. Even when it comes to a group-level context where the

composition of the social context beyond a source-receiver relationship must be

influential to the communication process, the concerns about the interdependence among

individuals have been largely disregarded due to the difficulty of measurement. The

academic predisposition to psychological exploration with individual unit of analysis

over social and relational structures plays a part as well (Barnett, Danowski, & Richard,

1993).

A full-fledged exploration of Web 2.0-based interpersonal influence, however,

requires attention to the social contextual effects on a user’s attitude or behavior. While

much of the past CMC literature focuses on the one-to-one communication situation or a

a small group with a limited boundary (e.g. Lee, 2006; Walther,1992, 1996; Walther,

Anderson, & Park, 1994), the spread of social influence through online social networks in

recent years is frequently not constrained to the dyadic context. Rather, it occurs in a

richer, more complex relational web. In other words, the scope of social information is

far broader on Facebook than offline or in older forms of CMC context. Extended social

information on Facebook is the combined product of interpersonal, group, and

broadcasting communication features. While the original sender of the message exists,

interpersonal visibility on Facebook is not exclusive to that original sender, but rather, it

embraces information about attitudes or behaviors of many networked others. In this

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sense, composition of social networks needs to be seriously considered in order to

understand the process of social influence on Facebook. Fortunately, studying these

constructs on Facebook helps researchers lessen the burden of collecting structural data,

which often hinders offline influence studies from investigating social contextual effects

even if researchers are aware of its significance.

This dissertation attempts to supplement the limitations of preexisting literature.

To consider the social context effect, I look at how individuals are embedded in larger

personal networks online. This is possible by taking advantage of Facebook data and

utilizing concepts and measures developed in social network analysis. Social network

analysis is based on sociometric data that conveys relational information among pairs of

actors in a social system. This project could take advantage of the web-based Facebook

application that allows users to extract the sociometric information about friendship

networks (i.e. who are friends with whom). In chapter three, I describe how structural

exploration of personal networks can be enriched through Facebook sociometric data.

Chapter Four

This chapter explains how the project was conducted. To overcome the limitation

of the perception-based approach to social influence, I utilize observation data about

individuals’ actual social interactions and behavioral outcomes. A cyber-field experiment

was conducted by combining various methods of data collection, including the

behavioral-tracking experiment, the conventional survey, and computer-automated data

extraction. The project procedure is described in detail. The preliminary analysis of the

results of the project is also reported.

Chapter Five

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From chapter five to chapter seven, I discuss the empirical analyses conducted to

explore the behavioral social influence processes on Facebook. Each chapter is written in

a complete format whose subsections consist of the background, hypotheses, method,

results, and conclusion. Rather than being independent projects from one another,

however, each chapter is a portion of the entire project.

The fifth chapter is based on the individual level of analysis with each message

sender as the unit of analysis. In this chapter, I attempted to identify those so-called

influentials among college students on Facebook with an emphasis on defining their

social characteristics. To do so, I borrow existing indicators of opinion leadership

developed in an offline context and associate them with my subjects’ actual capability to

mobilize their Facebook personal networks. Furthermore, I tried to operationalize their

social attributes from a structural perspective, particularly based on the characteristics of

Facebook personal network structures. Given that measurements of personal network

structures are developed from graph theory which is relatively new to CMC studies, I

provide formalized definitions of each measure in detail.

Chapter Six

The unit of analysis in the sixth chapter is not message senders but receivers. In

this chapter, I question whether individuals’ positions in a larger Facebook relational

network produce varied levels of interpersonal influence that motivates individuals to

enact a certain behavior. This chapter highlights structural social influence

conceptualized by three components: direct contact, contagion, and cohesion (Meyer,

1994). Furthermore, interaction effects among the contagion sources are tested to see if

the influence is synergized when multiple structural properties occur together.

12

The unit of analysis is still the individual as in the fourth chapter. However, I

argue that the analytic level in this chapter should be understood as meso-level in that

structural properties of each individual can be measured only when the presence of the

other actors is assumed within the network. Therefore, individuals are understood as

interdependent rather than independent of one another. This interdependent relational

context demands that a researcher should expect the possible correlations among the

observed cases, which violates the assumption of independence of observation. The issue

of dependency is addressed in the methods section, providing an alternative approach to

the conventional standard statistical testing.

Chapter Seven

The seventh chapter covers the most macro perspective for exploring the

structural patterns of the Facebook interpersonal influence. On Facebook, there are many

sub-communities, or groups, that emerge through spreading information such as peer

recommendations or computerized recommendations. The process of information

spreading is referred to as word-of-mouth (WOM) processes. As a result of the

experiment conducted in this project, I observed the formation of a new social network

among like-minded people regarding the advocated message.

The purpose of this chapter is to find whether the emergence of sub-communities

in Facebook through WOM processes shows any systematic structural pattern. Three

structural patterns are considered: scale-free network, small-world structure, and network

centralization. Well-established literature supports that real networks in social life do not

randomly emerge (Barbarasi, Wattz, & Strogatz, 1998; Moody, 2004). These three non-

random patterns are the prominent structures found in real social networks. After

13

explaining the characteristics of each network pattern, I posit the hypotheses along with

the patterns, and test which structural pattern is prominently observed from the emerged

advocacy group on Facebook. By looking at the emergence of structure, I attempt to link

the structural perspective to the spread of behavioral influence within a social system.

Chapter Eight

Finally, chapter eight summarizes the three empirical chapters. Although each

chapter deals with different aspects, the investigation targets one phenomenon occurring

from the single experiment. As mentioned earlier, a multilevel perspective is required to

fully understand the dynamics of social networking process on Facebook. By assembling

the pieces into a big picture, I attempt to integrate the findings of each chapter into a

theoretical frame of the Web 2.0 influence. The limitations of the project are also

addressed. Considering the contributions and limitations of the study, I propose the next

step to expand the project.

14

II. THEORETICAL BACKGROUND

2.1. Influence in Strategic Communication: Personal versus Social Contextual Influence

In strategic communication, interpersonal features of influence have been studied

in two major traditions. In one, interpersonal influence is conceptualized as influence

from individuals who possess attributes of “influentiality.” Influentiality has been defined

in various ways depending on topical interests. In persuasion studies, for example, source

influence is measured based on the source’s credibility as perceived by receivers

(Cialdini, 1993). In campaign studies, personal influence has been a mediator or

moderator of mass media effects (Southwell & Yzer, 2007). The attribute of personal

influence in campaign studies is defined as opinion leadership and is measured with

indicators such as Weimann’s personality strength (1994) and King and Summer’s scale

(1970). Sometimes, opinion leadership is associated with social characteristics such as

cosmopoliteness, gregariousness and information seeking behaviors (Katz, 1957; Nisbet,

2006; Rogers, 1995).

Opinion leadership is closely related to the individual aspect of the influence

mechanism of strategic communication such as public campaigns and marketing.

According to Kramer, Brewer, and Hanna (1996) and Scheufele and Shah (2000), trust in

opinion leaders results in effective social influence. Scholars understand that social

influence produced by contact with opinion leaders is informational influence

(Henningsen, Henningsen, Cruz, & Morrill, 2003) in that, as both active information

seekers and givers, they tend to be perceived as more credible and helpful for a high-

15

quality decision making by non-opinion leaders (Nisbet, 2006). A detailed discussion on

the literature of opinion leadership will continue in chapter 5.

Social structural aspects are another mechanism of influence. This line of

research suggests that social influence should be driven by an actor’s cognitive

processing of information about others’ attitudes or behaviors. According to Festinger

(1954), social influence is the result of social comparison: individuals want to change

their attitudes or behaviors to align themselves with the reference person or group with

whom they compare themselves. Bandura’s social cognitive theory (SCT, 2001) similarly

highlights that learning by observing their social environment exerts influence on

individuals’ attitudes or behaviors. Marsden and Friedkin (1993) call such social

influence interpersonal visibility, defining it as the extent an actor has information about

other actors’ opinions, attitudes, or behaviors (Friedkin, 1998). Within an organizational

context, the social information processing model similarly proposes that individuals’

attitude toward the job or adoption of organizational technologies is not free from the

influence from co-workers with whom the individual interacts (Salancik & Pfeffeer, 1978;

Fulk, 1991). Chapter 6 discusses the structural social influence in-depth, with a particular

emphasis on the context of WOM communication.

2.2. Network Approach to Influence: Personal Influence and Social Contagion

Applying network analysis to interpersonal and social influence studies offers a

unique contribution to both traditions. While the conceptualization of influence in both

traditions is often based on the measurement of individual perception or personal traits,

the individual level of assessment does not uncover the essential mechanism of influence,

which is interpersonal. Interpersonal relationships are not individual traits. The properties

16

of the interpersonal relationship should be understood on a meso-level, which is

systematically explored through social network analysis. Social network analysis of

interpersonal influence helps scholars understand how relational structures affect the

process of influence.

The network approach has been adopted in both traditions. In opinion leadership

literature, the sociometric approach has been one of the main methods of identifying

opinion leaders. If an actor is designated by many others as the provider of advice or

information about an issue, the centrality of the actor in the social network is high. The

centrality represents the actor’s opinion leadership. According to Rogers (1995), the

sociometirc technique is “a highly valid measure of opinion leadership” although it is

applicable appropriately only when “all (or most) members of a social system provide

network data” (pp.309-310). The high validity of the sociometric approach is also

supported by Weimann (1991), who utilized the network-based measure as the reference

indicator of opinion leadership to test the validity of other measurement based on the self-

designating method. Applications of sociometric data to opinion leadership studies are

widely found in classical diffusion studies such as Coleman, Katz, & Manzel (1957) and

Becker (1970).

Social influence driven by network positions has been studied in other contexts as

well. For example, in the organizational context, the social information processing model

has been incorporated with network analysis, finding that an individual’s job satisfaction

is affected by network proximity, which produces localized social influence and

centrality, and which signifies the individual’s power position in the organization’s

informal and formal networks (Ibarra & Andres, 1993; Rice & Aydin, 1991). Pollock,

17

Whitbred, and Contractor (2000) measured social environment influence by summing

social contacts’ job satisfaction scores weighted by the communication frequency to

which a respondent has with each of them. They found that social environment influence

accounted for an individual’s job satisfaction in addition to job characteristics. Social

influence, however, was moderated by individual personality, particularly the self-

monitoring tendency.

It is manifest in the network approach that social influence occurs within social

context. Network scholars articulate that the emergence of normative attitudes or

behaviors is the result of social influence that is spread through interconnected members.

The contagion model (Burt, 1987; Valente, 1995; Meyer, 1994) is a representative model

that explains network effects on individuals’ decision-making. According to the

contagion model, the spread of normative attitudes or behaviors is not the consequence of

direct demand from a particular source but of learning and imitation driven by

information visibility (Friedkin, 1998).

Therefore, structural properties within communication networks are highlighted as

important predictors of contagion effects. For example, cohesively interconnected actors

are likely to behave in a similar way because members in a cohesive sub-group are likely

to share similar information or feel more intense peer pressure from one another.

Alternatively, even though not directly connected, two people who communicate with the

same individual can also behave similarly because they are influenced by the same

person’s behavior. For example, if two professors are chairs in different departments,

they are not likely to communicate directly to each other. However, their decision-

making process can be similar to each other’s because their positions are equivalent,

18

including similar job descriptions and social interactions with the same personnel such as

the academic dean and provost.

2.3. Interpersonal Influence on User Behaviors on SNS

The structural approach to interpersonal influence can be applied to behavioral

influence occurring in SNS. Prior to applying network concepts to the phenomena of

interest, it is necessary to characterize the sub-processes that cause interpersonal

influence on SNS users’ behaviors. For some cases, behavioral changes or modifications

in SNS may accompany a cognitive commitment, as suggested by Bandura’s SCT. In

other cases, cognitive involvement may not be the preceding process for a behavior to

occur, such as in the case of herding or bandwagon behaviors and information cascade

(Danowski, Gluesing, & Riopelle, in press; Easley & Kleinberg, 2010). As exemplified in

Chapter 1, it is prevalent in SNS that the enactment of a certain behavior is instantaneous,

thus does not necessitate a learning process and the subsequent attitude change process.

Such behaviors are analogous to compliance in one-to-one persuasion context.

In both kinds, behavioral influence in SNS is unique when compared to traditional

forms of electronic social influence in that it simultaneously accompanies both direct

contact effect and social environment influence. In a traditional CMC context, a person

who receives a message demanding compliance tends not to display information about

other recipients’ responses unless the recipients communicate with each other about the

message. For example, let us suppose that one of my friends sends me an email saying,

“It will be the happiest week in your life if you pass along this message to 10 people.”

Even though I might know who else also received this message if the friend has emailed

me on a Carbon Copy (CC), I would be still blind to the information about who actually

19

enacted the pass-along action. If I want to know who did, I need to get in touch with the

other friends and ask whether they did the action. Let us suppose that the same scenario is

applied to a Facebook context, replacing emails with profile wall-posting. On Facebook, I

do not have to engage in separate communication with other friends to acquire the

information about their actions, because I receive RSS feed whenever my friends do

something to their profiles. Even if I missed the feed, I can conveniently check my

friend’s profile and see how they responded to the message. In other words, Facebook

offers an easy access to social information of others’ behaviors without necessitating

extra effort to communicate with others. Thanks to the greater accessibility to social

information, it is likely for users to perceive greater normative influence than through

older forms of CMC. From the network perspective, the normative influence through

online social networks is rephrased as users’ susceptibility to “contagion” effects (Burt,

1987).

However, influence in online social networks is not considered as merely a

contagion effect. This is because behavior contagion assumes that an actor’s behavioral

engagement occurs “in a social interaction in which the informational source has not

communicated intent to evoke such a change” (Polansky, Lippitt, & Redl, 1950, p. 322).

Accordingly, contagion is assumed to come from the social environment rather than from

a direct interpersonal request. Unlike this assumption, many SNS behaviors are enacted

as a result of compliance to peer recommendation or suggestion, as seen in the scenario

above. Although the recommendation system is widely adopted in other online contexts

such as shopping websites, many of these tend to be computer-automated or anonymous

recommendations. Therefore, interpersonal influence might intervene in the actor’s

20

decision process less than the perceived utility of the product or action. In SNS, however,

a recommendation can convey even larger personal influence than the recommendation

services found in online shopping websites because these suggestions do not come from

an anonymous recommender but instead from an actor’s real friend. Therefore, personal

influence from a message sender could also be an important determinant of social

behaviors on Facebook as well as a contagion effect.

In sum, Facebook’s social influence should be understood as a mixture of one-to-

one personal influence and the normative influence inherent in a social environment. To

capture the dynamics of social influence occurring in Facebook, a researcher should

explore both personal influence and the contagion effect. To do so, one should embrace

multi-level perspectives in which both individual attributes of direct contacts and

characteristics of social contexts are subsumed.

21

III. SNS ENRICHES PERSONAL NETWORK ANALYSIS

3.1. Sociometric Data on Facebook

The purpose of this project is to explore individual and network effects on

behavioral influence on Facebook. As described above, the behavioral influence on

Facebook is spread through the combined process of personal influence and social

informational influence. Given that personal influence comes from direct contact with the

message sender, it is analogous to compliance, which is a widely discussed sub-process

of social influence (Cialdini & Goldstein, 2004). On the other hand, social informational

influence is defined as a network structure effect in that the information must be

embedded in the structure of Facebook personal networks. Considering the dual

mechanism of compliance and the network effect, this project attempts to adopt multi-

level perspectives ranging from the individual attribute-based approach to the macro-

level of structural analysis, to the examined topic.

Observing behavioral influence is not an easy task. Influence scholars interested

in behavioral influence can take advantage of an online environment in which user

activities are traceable in a real life context. Facebook is an online community

particularly useful for multi-level investigations thanks to its technological affordances.

In 2007, Facebook opened an application programming interface (API), allowing outsider

programmers to develop applications that would integrate with Facebook. Sociometric

data, which is burdensome to collect manually, are also accessible using the applications

developed based because of the open API. Along with sociometric data, higher order

22

predictors of social influence, such as actors’ positional properties within the network and

structural properties of the network as a whole, can be explored.

Since the introduction of the first generation of SNS such as Friendster in the

early 2000’s, SNS have been adopted across all ages so rapidly that they have become the

most popular sites among contemporary online services. Numerous SNS appear not only

in the US but also on a global level, e.g. Qq in China, Orkut in Brazil, Cyworld in Korea,

Badoo in London, and Studivz in Germany. Furthermore, niche SNS have also emerged,

such as for professional networking (e.g. LinkedIn, Academia) and for like-minded self-

improvers (e.g. 43things). The history of the development of these SNS is well

documented in the literature (Boyd & Ellison, 2007; Boyd, 2008; Rosen, 2007). Among

the various types of SNS, the use of Facebook is currently one of the most popular online

practices. According to the web information company Alexa Internet (March 26, 2010),

among more than one million global websites, Facebook holds the second-to-top traffic

rank with 776,492 websites hyperlinked to it.

One of the most distinctive characteristics of Facebook is that it integrates a wide

variety of interpersonal relationships emerging on- and offline. Although not the perfect

representation of the complete personal network, the so-called ‘friend list’ in SNS is

understood as the best intensive representation of active personal networks more than any

other approach, at least in terms of peer relationships. The accumulated social

connections on individuals’ profiles and various relational tools offered by Facebook,

such as notification service of a forthcoming friend’s birthday, help a user maintain

weak-tie relationships as well as strong ties with less cognitive effort and time spent.

Considering that cognitive and time constraints have been the primary barriers that limit

23

the size of active personal networks (Robert, Dunbar, Pollet, & Kuppens, 2009; Hill &

Dunbar, 2003), SNS is a useful time saver for maintaining relationships.

Along with the multitude of personal relationships displayed in a user’s profile,

Facebook technologically allows users to analyze their own complete network, which

includes not only the connections between the user himself and the configured social

contacts, but also among social contacts themselves. The full visualization of personal

networks offered by Facebook is especially attractive to social network scholars because

of the potential to integrate the egocentric network data into the whole network analysis.

3.2. Egocentric Network Analysis: Limitations of Preexisting Methods

Studies on personal community have been developed based on egocentric network

designs that assemble relational data composed of a focal actor (ego) and the focal actor’s

interpersonal relations (alters; Marsden, 2005). The conventional method for data

collection for egocentric network designs is to use name generator instruments. The

instruments are composed of “name generators” that are “free-recall questions that

delineate network boundaries” and “name interpreters” that “elicit data about alters and

both ego-alter and alter-alter relationships” (Marsden, 2005, p. 11).

Two limitations regarding the data for egocentric network studies have been

discussed: First, the data tend to underestimate weak ties. A personal network is

composed of a broad scope of social ties that a person has encountered and interacted

with throughout his or her lifespan (Marin & Hampton, 2006). Although the network size

and compositions may vary depending on individual traits and socio-demographic factors

(Robert, Dunbar, Pollet, & Kuppens, 2009), scholars have indicated that the global size of

personal networks is generally quite massive. Roberts et al. (2009) discuss that the crude

24

distinction between Granovetter’s (1973) strong and weak ties can be elucidated into the

“concentric circles of acquaintanceship” (p.138) with ego sitting in the center of the circle

(Figure 1). Specifically, the innermost layer, called the “support clique,” includes about 5

people with whom an ego maintains the most intimate and strongest attachment,

circumscribed by the next layer, called the“sympathy group,” consisting of 12-15 close

friends and family members (Robert et al., 2009, pp. 138-139). The majority of personal

network studies sets the boundary of social contacts as those who are identified in the

support clique or in the sympathy group at best (e.g. Campbell & Lee 1991; Fu 2005;

Hampton and Wellman 2003; Marsden & Campbell, 1984; Wellman & Wortley 1990).

Focusing on support cliques and sympathy groups lends researchers to tilt in favor

of a strong ties-based examination of personal relationships. However, it is evident that a

good portion of personal networks is composed of weak ties. Weak ties are often an

important relational reservoir in that a focal actor has access to novel information and

instrumental social capital through weak ties (Burt, 2001; Granovetter, 1973; Lin, 1999).

Despite the importance of weak ties as a part of network composition, it has not

frequently been highlighted, primarily due to the large network boundary and the

difficulty in generating names (Marin & Hampton, 2006). Because of the failure to

include weak ties, the structural level of social network measurements that assume that

the network includes the complete list of actors and relational information within the

boundary are not appropriately explored with full egocentric network data.

Zhou, Sornette, Hill, and Dunbar (2005) find that a distinct layer of relationships

emerges at around 50 members in personal network, termed a band. A band is the

intermediary between the layers of strong ties and of weak ties. Roberts et al. (2009) term

25

the layers of weak ties an “active network,” referring to social relationships an ego feels

that he or she has a personal relationship with, and makes a conscious effort to keep in

contact with (pp.138-139). Various possible sizes of active network were proposed.

Kilworth and Bernard (1978) used a method called the reverse-small world technique,

resulting in the size of about 250. McCarty, Kilworth, Bernard, Johnsen, and Shelley

(2001) used two different methods – scale-up and summation method, finding an average

personal network size of about 280-290. As a more stringent approach, Hill & Dunbar

(2003) reasoned that the contact list of people to whom Christmas cards were sent is a

valid indicator to estimate active size. This approach produced personal networks with a

mean of 154.

The last layer of the concentric circle is called the “global network” (Roberts et al.,

2009, p.138-139). The global network layer includes those whose faces an ego can

recognize. Scholars introduced amazingly large numbers for the size of this layer, for

example, 1500-2000 (Stiller & Dunbar, 2007), 1700 ± 400 (Killworth, Johnsen, Bernard,

Shelley & McCarthy, 1990), and even reaching up to 5000 (McCarty, Bernard, Kilworth,

Shelley, & Johnsen, 1997). The global network is distinguished from weak ties in that

the members in this layer are not necessarily active in terms of social interactions. Instead,

they have the potential to be activated under certain opportunities or motivations. For

example, let us say that a focal actor, George, is superficially acquainted with Mike, who

is a friend of John’s friend Frank. Although Mike is simply one of the thousand members

in George’s global network, Mike has a chance to move into George’s active network if

Frank introduces him to George at the party Frank threw. In other words, members in a

global network are conceived as “latent” (Haythornthwaite, 2002), who are currently

26

hibernates yet have potential to be converted into active relationships of weak or strong

ties.

Figure 1. Layers of Personal Network1

The number of weak ties, regardless of the global network size, presents a

humongous task for a focal actor to complete the relevant network survey if the survey

asks a respondent not to just list alters’ names but also to answer the relational states

between pairs of alters. Considering that finding out information about alter-alter

relationships should include N*(N-1)/2 numbers of questions (N = number of alters), the

expected time consumption to complete the survey increases multiplicatively as the size

1 Partly Adapted and Modified from “Exploring Variation in Active Network Size,” by S. Roberts, and R. Dunbar, January 2008, Paper presented at SUNBELT conference, St. Petersburg, FL.

27

of the network grows. Subsequently, data collection for a comprehensive egocentric

network composed of both strong and weak ties is unrealistic.

Another limitation that preexisting name generator instruments pose is the

possibility of egos’ imperfect recall. Inaccurate recall has been a “non-trivial”

methodological issue within personal network scholarship (Butt, 2003, p.107). For

example, Brewer and Webster (1999) found that survey respondents failed to recall 3% of

their best friends, 9% of their close friends, and 20% of their relatively weak ties (Lackaff,

2010). Forgetting and providing inaccurate information is misleading when measuring

structural properties of networks. The broader the personal network boundary is set, the

more aggravated the inaccuracy problem.

These two limitations often hinder personal network researchers from exploring

macro-level network properties for which measurements are developed based on the

complete or whole network analysis. In this sense, the access to SNS data, including

Facebook, is revolutionary for personal network research. Aligning with scholars who

have advocated the advantages of web-based instruments for egocentric network data

collection (e.g. Gosling et al., 2004; Hogan et al., 2007; Vehovar et al., 2008), using

Facebook data helps a researcher to explore the full-fledged ego-networks in several

ways (Hogan et al., 2007; Lackaff, 2010). First, the socio

metric data among alters are easily accessible by using Facebook API-based

software. On average, Facebook friendships consist of more than 300 individuals (Wang

et al., 2010), exceeding the size of active networks proposed by previous studies. Second,

the information about social connectedness is a well-acknowledged parameter of

producing a social network (Marsden, 2005). While the information about connectedness

28

can be inaccurate when dependent on the informant’s memory, online archives of

connectedness is hardly misled by inaccurate recall. Sociometric data form Facebook is

credible in this sense (Hogan, 2007). Lastly, even richer information about relationship

quality can be objectively measured without filtering the informants’ perceptual bias.

According to Easley and Kleinberg (2010), the histories of social interactions

accumulated on Facebook profiles offer a rigorous form of measurement to the extent of

communication frequency or emotional strength.

In sum, Facebook data are based on more reliable and valid information than

traditional approaches in that the network includes a broader range of alters and the

interpretation about alter-alter relationships is less susceptible to informants’ imprecise

memory and perception. Unfortunately, however, few studies have actually taken

advantage of Facebook network data to capture the process of social interactions within

personal networks (Lewis, Kaufman, Gonzalez, Wimmer, & Christakis, 2008).

According to Lewis et al. (2008), the majority of Facebook literature has framed the

relational information found in users’ profiles as one strategy of self-disclosure and

impression management. It has been regarded as the profile owners’ individual attributes

(e.g. Boyd, 2004, 2006; Boyd & Heer, 2006; Donath & Boyd, 2004; Fono & Raynes-

Goldie, 2006; Rosen, 2007; Skog, 2005; Wang, Moon, Kwon, Evans, & Stefanone, 2010;

Walther, Van Der Heide, Kim, Westerman, & Tong, 2008). Likewise, studies exploring

the benefits of having large personal networks in SNS tend to exclusively count on

conventional survey methods, disregarding the structures of relational ties among users

(e.g. Ellison, Steinfield, & Lampe, 2007). Despite the explicit articulation of personal

29

networks configured on Facebook, it is still premature to incorporate the structural

perspective into the study of user behaviors on Facebook.

30

IV. PROJECT DESCRIPTION

4.1. A Cyber-Field Experiment: Mobilizing a Campus Advocacy Network on Facebook

This dissertation project is based on a cyber-field experiment that mobilizes

college students for a campus advocacy group on Facebook. At the end of 2009, four

undergraduate students at the University at Buffalo (UB) who were members of a student

government organization and were not satisfied with the library conditions created a

Facebook group called “Students who want better UB libraries.” The aim of the group

was to raise students’ awareness about the poor conditions of campus libraries and to

allow students’ opinions on ways it could be improved to be heard by administrators if

needed. Initially, it was a small, self-organized advocacy group of 34 members. Finding

the group suitable for this project, I contacted group administrators and asked their

willingness to expand the group through collaboration with this dissertation project.

The field experiment was conducted in this way: After getting confirmation from

the student government and the IRB, confederates were recruited from large

undergraduate lectures to play “opinion leaders” for the virtual advocacy action in

Facebook. The qualifications to play an opinion leader were (1) to have a positive stance

with the aim of the group’s advocacy, (2) to be willing to recommend that their Facebook

friends support the advocacy and (3) to have at least 50 Facebook friends, at least 20 of

whom are UB-affiliated. Students who played opinion leaders got one hour of research

credit as a reward. Playing an opinion leader was voluntary, as non-players were

provided with many other alternatives to obtain the equivalent amount of credit. While

31

the players were also the subjects to be analyzed in Chapter 4, they were treated as

experimental confederates who were not included in the analyses in Chapters 5 and 6.

The advocacy group was open to the UB public, meaning that anyone could view

the content in the group regardless of membership status as long as they were affiliated

with the UB network. At the beginning, the group had 10 photos that showed the poor

conditions of the UB libraries such as empty book shelves, untidy study rooms and

unclean restrooms, as well as two discussion threads asking for students’ wish lists for

the libraries and their opinions about how to improve the conditions. The main page, or

so-called ‘group wall,’ had a few of postings notifying viewers of the updates. The wall

later became a very active discussion forum accompanying group size growth. Figure 2

shows screen snapshots of the group as of March 2010.

32

Figure 2. The Snapshots of the Advocacy Group Emerged from the Project

33

4.2. The Role of Opinion Leader Players

The role of opinion leader players was to spread the information through the

advocacy group and recommend that their friends join the group to show support. To do

this, they would send messages to their friends through a Facebook tool called “group

invitation.” The tool allows a group member to gain access to his or her own friends list,

select the target friends to whom the person wishes to send a customizable invitation. To

control the variability of invitation content that might reduce internal validity, players

were asked to use message content that was customized by the researcher. Players were

guided to select and send the message to all of their friends who were identified as UB-

affiliates. Selecting UB-affiliated friends was easily enacted through the filtering option

afforded by the tool. If another player was selected in the list of invitation recipients, he

or she was removed.

After sending the invitations, players additionally participated in a survey asking

about their own demographic characteristics, opinion leadership and personality and

social characteristics. With their permission, I also gained access to the data describing

their egocentric networks in Facebook. The network data are composed of two types of

information. The first is simply a list of friends’ names, analogous to the data collected

from “name generators” in conventional egocentric network instruments (Marsden, 2005,

p.11). The name list was used to trace which new members received a recommendation

from which confederate. This procedure was done manually by matching the group

members’ names with the names identified in the list. The second type of information is

sociometric data about who-knows-whom. This is analogous to the data collected from

“name interpreters” in the egocentric network instruments (Marsden, 2005). The first row

34

and column of the matrix display all the friends’ names who received the invitation

messages and each cell identifies whether a pair of friends share a connection on

Facebook.

The sociometric data were used for three purposes: one, to characterize the

structure of each confederate’s personal network; two, to identify the positional

properties of each alter within the confederate’s personal network; and three, to capture

the structure of the emerged advocacy network as a whole. The sociometric data includes

only UB-affiliated friends.

4.3. Terminology Summary

Hereafter, I use various terms interchangeably to denote opinion leader players

and their Facebook friends. Some terms are contingent on this project, some from

personal network literature, and some from the mathematical terminologies used in social

network analysis. Table 1 summarizes the terminologies that will be used throughout the

remaining chapters.

35

Table 1. Terminology Summary

Basic Term Interchangeably used with…

Source

Opinion Leader Players Confederates, Inviter, Recruiter, Recommender, Message Sender

Project specific: Recruited by a researcher for the purpose of experiment to observe subjects’ behaviors

Opinion Leader Project specific: Used to discuss about personal influence in Chapter 4

Ego Egocentric network terminology: used to refer a focal actor or owner of the personal network

Players’ Facebook Friend

Invitee, Recruitee, Message receiver (recipient)

Project specific: Targeted to recruit to the group by the opinion leader players

Alter Egocentric network terminology: used to refer ego’s social contacts

Actor, Node, Vertex(Vertices)

Social network terminology: used to refer to those whose names are listed in the first row and column of the socio-matrix

4.4. The Evolution of Advocacy Network: A Brief Report

The increase in group membership and activities was tracked over a span of six

days since the opinion leaders’ action began (from 2pm January 25, 2010 to 2pm January

31, 2010). A total of 132 opinion leaders sent recommendations to 7,486 uniquely

identified UB-affiliated friends. After the week, the group size increased from 34 to 2,038

members.

Three days after the project began, the advocacy group received coverage on the

front page of the campus newspaper. The news article also included interviews from

36

several administrators including the director of the library’s technology department. The

next day, some administrators also joined the group. Figure 3 shows how rapidly the

group size increased within a week.

Figure 3. Over Time Increase of Group Membership, from January 25th to January 30st,

2010

One interesting point is that the aim to have student voices heard by

administrators was achieved in only three days since the action began. Fast diffusion is a

common phenomenon when information and communication technology (ICT ) takes a

major part in the process of propagation. The resulted diffusion curve is characterized as

“r-curves” (Henrich, 1999) which “begin convexly with the maximum growth rate and

then slowly taper off toward equilibrium” (Danowski, Gluesing, & Riopelle, in press,

0

500

1000

1500

2000

2500

1/24 1/25 1/26 1/27 1/28 1/29 1/30 1/31 2/1

Mem

bers

Time

37

n.p.). This curve challenges the classical Bass model (Bass, 1969) which displays an S-

shaped cumulative adoption curve (Rogers, 2003).

Geroski (2000) and Rosenkopf and Abramson (1999) explain that the rapid

growth r-curve results when the diffusion process includes the bandwagon or herd

behavior effect, which occurs when (1) more information about the number of adopters is

available and (2) less learning process is required for adoption decision. These conditions

are met in many ICT-enabled communication situations because, first, messages about

others’ adoption behaviors are more rapidly and more easily spread than in face-to-face

communication and second, many online behaviors or innovations often do not require so

much cost or commitment to go through a serious learning process prior to making an

adoption decision. For example, Danowski et al. (in press) show that e-mail based

diffusion of innovation within an organization was better explained by an r-curve than an

s-curve.

The diffusion process resulting from this project can also be understood to include

the bandwagon effect in that the two conditions were met in this case. Specifically, as the

most cutting-edge ICT that maximizes the visibility of social information, Facebook

easily exposes users to the others’ adoption behaviors. Also, the advocacy group publicly

opened its page so that Facebook users could see, from time to time, how many members

the group had. Furthermore, joining the group was not a difficult task. Little cost or time

consumption was required for an actor to join the group. Accordingly, actors could

decide whether to comply with the recommendation to join or decide not to join without

in-depth learning about the advocated action.

38

Barnett (in press) explains that diffusion can be described as a convex r-curve

when the external source of influence predominates over the interpersonal

communication process. He states that the diffusion curve “begins convexly with the

maximum growth rate slowing only when most potential adopters have adopted” when

“the external agent forces adoption nearly simultaneously” (n.p.). Considering that the

recommendation message was sent out almost simultaneously by the confederates who

are outside of the pool of potential adopters, Barnett’s discussion also support the

possibility of an r-shaped evolution of the group.

I estimated increase of the membership curve by reordering the data every two

hours. Both Sigmoid (the representation of the s-curve) and Cubic curves (the

representation of the r-curve) were tested within that period of time as an independent

variable and the cumulative membership as a dependent variable. While both curves were

statistically significant, the larger variance was better explained by the cubic model (R2

= .97) than by the sigmoid model (R2 = .89), supporting the existence of a convex r-curve

emerging during the group evolution (Table 2). Figure 4 plots the observed and the

expected curve.

Table 2. Curve Estimation for Group Evolution

Model Summary Parameter Eestimates

R2 F Sig.

Constant b1 b2 b3 Cubic .97 487.61 .000 23.94 134.32 -3.14 .03 Sigmoid .89 446.72 .000 7.65 -5.04

39

Figure 4. Plots of R- and S-Curve

The group evolution also implies that the group information was passed along

multiple steps of relationship chains beyond the initial contact made by confederates. In

other words, only 43.33 % of the total group members (N = 883) turned out to be the

direct recipients of group invitations from the confederates of the experiment. Excluding

34 original members and 132 confederates, the rest of the members (N = 989) joined the

group through other informational channels: They could have heard about the group

through the campus media or through the chains of WOM spread throughout Facebook

networks. The success of the advocacy group can also be attributed to the bi-directional

effects between the increase in membership and the increase in group activities. Indeed, I

observed discussions had been actively developed during the week. Simple statistics tell

40

that a total of 137 discussion threads, 47 wall postings, 63 sub-comments, and 93 likings

were shared in a week since the action started.

It is noteworthy that the evolution of the Facebook advocacy group followed the

convex r-shaped curve, which has recently been highlighted as the distinctive pattern for

ICT-enabled diffusion from the diffusion through face-to-face interpersonal

communication. The diffusion process might be due to the direct social contacts but also

to potential actors’ exposure to the information about the membership growth and the

intensity of group activities. Also, the evolution of the advocacy network was inherently

a self-organizing phenomenon, displaying voluntary interactions among the members.

However, these evolutionary aspects are beyond the scope of this dissertation and are

thus reserved for future research agendas.

41

V. THE FACEBOOK INFLUENTIALS: ON THEIR SOCIAL NETWORK

CHARACTERISTICS

5.1. Who are the Influentials?

5.1.1. Profiles of Opinion Leaders

In this field experiment, the most explicit source of influence for students to be a

part of the advocacy action is the direct recommendations made by the opinion leader

players. Because the researcher was blind to each player’s social and personal

characteristics when recruiting opinion leaders, their individual capacity to mobilize

members would be dissimilar to one another. Those who exert greater influence on others’

attitude or behavior can be defined as the influentials, or opinion leaders.

Opinion leadership has been widely studied within the contexts of marketing,

political communication and health campaigns. According to the classical theory of two-

step flow, the effect of media messages is not directly applied to mass audiences but

instead is moderated or mediated by interpersonal communication (Katz, 1957; Southwell

& Yzer, 2007). In the process of interpersonal communication, there is a subset of the

population called opinion leaders, who are more influential than the average population

in promoting behaviors or attitudes advocated by marketers or campaigners.

Opinion leaders are regarded as a valuable asset in the promotion of new products,

ideas, attitudes, and actions. Rogers (1995), a prominent scholar in diffusion studies,

emphasized that the change agents should be able to identify opinion leaders out of their

potential clients to expedite diffusion process. As Chan and Mistra (1990) elucidate,

personal influence exerted by opinion leaders becomes particularly important when word-

42

of-mouth communication is a critical component in consumers’ (or public’s in cases of

non-commercial sectors) decision making processes, in which forming a favorable

attitude or behavior toward the advocated object is more important than simply informing

untargeted mass audiences. Subsequently, identification or creation of opinion leaders has

risen as a major topic in diffusion literature (Chan & Mistra, 1990; Mancuso, 1969).

Profiling opinion leaders has been studied based on three categories of traits:

demographics, social and attitudinal traits, and product-oriented characteristics (Summers,

1970). Scholars contend that there is little overlap among opinion leaders of different

products as far as product-oriented variables (e.g. product experience, knowledge, skill,

or involvement) play a significant role in characterizing the influentials. Nonetheless,

decades of studies have documented more or less ‘universal’ profiles of opinion leaders

in terms of their personal and social attributes.

In particular, opinion leaders tend to reveal higher innovativeness (Gatignon &

Robertson, 1985; Myers & Robertson, 1972; Summers, 1970), less dogmatism

(Goldsmith & Goldsmith, 1980), higher self-confidence (Mancuso, 1969), and higher

public individuation (Chan & Mistra, 1990). Extensive studies have also indicated that

opinion leaders are consistently shown to be more gregarious, cosmopolite, and socially

active (for a review of the findings, refer to Weimann, 1990).

5.1.2. Measuring Opinion leadership: Four Approaches

Rogers (1995) defines opinion leadership as the “degree to which an individual is

able informally to influence other individuals’ attitudes or overt behavior in a desired

way with relatively frequency” and opinion leaders as “individuals who lead in

43

influencing others’ opinions” (p. 300). Evidently, an individual’s opinion leadership is

the direct indicator of his or her social position as the influential.

The measurement of opinion leadership has been proposed based on four methods:

Sociometric techniques, key informants’ ratings, self-designating techniques and

observation. Rogers (1995) and Weimann (1994) discuss the strength and weakness of

each method. First, the use of sociometric technique enables researchers to analyze real

social networks of information flow (about who gives and receives advice about an

innovation) among community members of interest. Given that opinion leadership is

measured through the respondents’ actual choices of receiving information, advice, or

influence, this method is a highly valid measurement. Unfortunately, this method is

burdensome to use because the researcher needs to have every member within a social

system to report social network data. Accordingly, data retrieval without missing cases

becomes less feasible as the community size increases.

An alternative method to the sociometric approach is to construct social networks

based on informants’ ratings. Key informants should be regarded as knowledgeable about

members’ relationships in a community of interest. Network data is formulated based on

the key informants’ nomination of individuals whom other people contact for information

or advice about a subject. While this approach is much more conveniently employed than

the sociometric approach, the data validity depends greatly on the selection of informants.

Unless a researcher does have enough of an insightful understanding about the target

community to identify knowledgeable informants, the validity is hardly guaranteed.

The self-designating technique is the most commonly employed method due to its

convenience. The exemplary instrument for the self-designating method is King and

44

Summers’ (1970) index, the modified version of Roger’s instrument (1995). King and

Summers’ index will be described fully in next section. While it is convenient to use, the

self-designating technique is the least valid measurement in that it depends on the

“accuracy with which respondents can identify and report their images” (Rogers, 1995,

p.311). Perception-based measurements can incorrectly estimate the relationship between

themselves and others, causing such problems in social projection as the “false

consensus,” which refers to the “overestimation for one’s own behavior” (Rice & Aydin,

1991, p. 221). Considering that opinion leadership is measured based on individuals’ self-

belief in their own expertise or knowledge, it is not surprising that opinion leadership

resulting from the self-designating technique tends to show high correlations with

personality attributes, such as confidence or innovativeness, which are also measured

based on self-evaluation.

Finally, observation is the most valid measurement of all four techniques (Rogers

1995; Weimann, 1991). The observation technique can be applied in situations where a

researcher can observe and record interpersonal interactions among community members

throughout the diffusion process. While it is seldom used in retrospective studies due to

the difficulty of data retrieval, the technique provides a researcher with the most certain

and richest information once it is possible to archive interactional history during a given

period of diffusion. The observation technique is advantageous in situations where a

researcher conducts a controlled trial of interventions or field experiments.

5.1.3. Profiling the Influentials in Web 2.0.

Rogers (1995) had pointed out the dual characters of the Internet: It is a one-to-

many process resembling conventional mass communication, but it has the personalized

45

nature of communication. Along with the technological progress that affords the

integration of the two modes of mass and interpersonal communication, the potential of

the Internet to facilitate word-of-mouth processes has been widely noted. According to

Sun, Youn, Wu, and Kuntarapon (2006), online communication intensifies the word-of-

mouth effect thanks to its asynchrony, one-to-many mode of communication, written

form of interpersonal interaction, speedy transmission of information, and easy access to

opinions of strangers as well as of acquaintances.

Recent academic interest in electronic word-of-mouth (eWOM) is reflected by

many studies that attempt to find recommenders’ psychological motives to exert

informational influence (e.g. Hennig-Thurau, Gwinner, Walsh, & Gremler, 2004; Phelps,

Lewis, Mobilio, Perry, & Raman, 2004; Sohn, 2009). Cakim (2006), the director of

Burson-Marstella Marketing Research, coined the term “e-fluentials” particularly to

denote the opinion leaders in the eWOM process. Cakim (2006)’s report suggests that

about 10 percent of online adult populations are categorized as e-fluentials, having an

impact on approximately 155 million consumers’ purchasing decisions. Noting the

potential role they could play in viral marketing online, a few studies attempted to

characterize e-fluentials, borrowing from the literature of traditional opinion leadership.

For example, Sun et al. (2006) tested opinion leaders’ personality and online social

attributes. They found that, while opinion leaders were defined as showing high

innovativeness, other variables such as product involvement and online social connection

were not significantly revealed. Lyons and Henderson (2005) found that online opinion

leaders are similarly characterized as their offline counterparts, showing high

innovativeness, involvement, and self-perceived knowledge. While these exploratory

46

studies initiated an important task to identify characteristics of e-fluentials, their findings

are preliminary to some extent due to the limited measurement of opinion leadership:

They measured opinion leadership exclusively based on the self-designated technique.

Accordingly, more valid techniques need to be employed to enrich the discussions of e-

fluentials.

Studying online opinion leadership is timely in the Web 2.0 environment.

In Web 2.0, the viral spread of influence is particularly prominent, often acting as the

major driving force for commercial, political and sociological changes. For example,

social context advertising has blossomed with the popularity of SNS, acting as the major

strategy for e-commerce and advertising revenues (Steel & Fowler, 2010, July 7). Also,

information spread through social media facilitates self-organized collaboration, as seen

in the example of Twitter use during the Haiti earthquake (Oh, Kwon, & Rao, 2010).

Examples exist to show how the WOM process lubricates successful collective actions

(Hintikka, 2008). Given the rise of social influence during eWOM communication, it is

important to profile opinion leaders in the Web 2.0 context.

5.2. Research Questions and Hypotheses

5.2.1. Aim of the Study

The aim of this chapter is to characterize the influentials in one of the most

popular Web 2.0-based services, Facebook. While considering other attributes such as

demographics and personality, the study particularly weighs social attributes configured

in the computer-mediated context, including online gregariousness, social activities, and

cosmopoliteness as important predictors of online opinion leadership. The special focus

on social characteristics is driven by the nature of opinion leadership inherent in its

47

definition. By definition, opinion leaders are the social influentials distinguished from the

innovators. Rogers (1995) suggests practitioners to be cautious not to mistake opinion

leaders for innovators. According to Rogers (1995), “opinion leaders have followers,

whereas innovators are the first to adopt new ideas and are often perceived as deviants

from the system’s norms…The innovators’ behavior does not necessarily convince the

average members of a system to follow suit” (p.388). Opinion leaders produce followers

“by serving as a role model that others can imitate, by persuading or convincing others,

or by way of contagion” (Nisbet & Kotcher, 2009). Attention to social attributes of

opinion leaders is also prominent in applied marketing or campaign contexts as reflected

by terms that practitioners coined, such as maven, buzzer, navigator, social connector, or

network hubs (e.g. Gladwell, 2002; Keller & Berry, 2003; Rosen, 2002; Sosnick, Dowd

& Fournier, 2006). Although other attributes are also important in conceptualizing

opinion leaders, this study is grounded on the concept that the social aspect is inherent in

a straightforward definition of opinion leaders as well as delineating general

characteristics of opinion leaders across different topical categories.

The study aims to uniquely contribute to the literature of online opinion

leadership in three ways: First, there is no precedence that characterizes opinion leaders

in the Web 2.0 environment. At the cutting-edge of computer-mediated social networking

practices, the question needs to be explored whether digital social networks amplify

opinion leader influence in accordance with offline influence or impair such influence

attributed to the poor quality of virtual interactions (Nisbet & Kotcher, 2009). To explore

such an inquiry, the preceding task is to characterize and identify who the influential

actors are online, second, while traditional opinion leadership literature relies on

48

respondents’ self-evaluation to construct the variables of social attributes, this study adds

behavior-based measurements to the preexisting instruments. To operationalize variables

of social attributes, I attempted to utilize the archived records in users’ Facebook profiles

about their interpersonal or group activities and actual social networks they maintain.

Three, distinctive to the preexisting studies of e-fluentials that are based on the self-

designating technique, this current study measures opinion leadership applying the

observation technique through the ‘cyber-field’ behavioral experiment. In addition, I try

to compare how opinion leadership constructed from observation and self-designating

techniques related to each other and suggest which might be a more valid instrument to

identify the Facebook influentials.

5.2.2. Hypotheses

As mentioned in the previous chapter, a field experiment was conducted by

recruiting opinion leader players. The players were chosen randomly. Therefore, it was

questionable who might be qualified to be the authentic influentials who would present

differentiable Facebook social attributes and contribute significantly to the successful

mobilization compared to the less influential players.

Personal and social characteristics are hypothesized against two criterion

variables: observed and self-designated opinion leadership. Observed opinion leadership

refers to the degree of behavioral influence, measured as the actual number of Facebook

friends each player mobilized through solicitation. For self-designated opinion leadership,

King and Summers (KS)’s index (1970) was employed. First, I examine whether

observation and self-designation approaches represent a common dimension of opinion

49

leadership. If they share same dimensionality, they will show high correlation to each

other. Accordingly,

H1. Observed and self-designated Facebook opinion leadership will be correlated

with each other.

Second, as in traditional opinion leadership studies, I hypothesize that personality

traits should contribute to the identification of Facebook opinion leaders. Specifically,

Weimann (1994) reviewed existing literature of opinion leaders’ personality

characteristics and integrated relevant items into the “Strength of Personality Scale (PS

scale)” (p.255). This scale showed a positive association with influenceability and

individuals’ sociometric network position and has been validated through many existing

studies (e.g. Nisbet, 2006; Nistbet & Kotcher, 2009; Scheufele & Shah, 2000; Shah &

Scheufele, 2006; Weimann, 1991; Weimann, Tustin, & Vuuren, 2007). Based on those

studies, the following are hypothesized:

H2a: Opinion leader player’s personality traits will positively predict the

observed opinion leadership

H2b: Subjects’ personality traits will positively predict their self-designated

opinion leadership.

According to traditional opinion leadership literature, social attributes are also an

important characteristics of opinion leaders (Katz, 1957). Equivalent to offline context

studies, Facebook-specific social attributes are expected to show significant association

with Facebook opinion leadership. Hypotheses are posited regarding social attributes,

including gregariousness, social activities, and cosmopoliteness. In regards to

gregariousness and social activities:

50

H3a: Facebook gregariousness will be positively related to the observed opinion

leadership (i.e. the behavioral influence subjects have on their Facebook friends).

H3b: Facebook gregariousness will be positively related to the self-designated

opinion leadership of the subjects.

H4a: Participation in Facebook social activities will be positively related to the

observed opinion leadership.

H4b: Participation in Facebook social activities will be positively related to the

self-perceived opinion leadership of the subjects.

The study takes a somewhat novel approach to operationalize cosmopoliteness.

Traditionally, cosmopoliteness has been indirectly measured, for example by the number

of places in which individuals have lived (Katz & Lazarsfeldt, 1955). Physical mobility

has been highlighted in operationalizing cosmopoliteness because of the assumption that

experiences with various local communities give individuals more opportunities for

diverse experiences and heterogeneous social contacts (Summers, 1970). This conception,

however, might not be appropriate in the online community in general as well as in the

special context of Facebook in that digital networks enable participants to engage in

heterogeneous social communication without the necessity of physical encounters.

Therefore, a different measurement of cosmopoliteness is required instead of highlighting

physical mobility.

Fortunately, more straightforward measures of cosmopoliteness are available

from personal network data in Facebook. Considering that the Facebook personal

network is constructed through social interactions between the network owner and his or

51

her Facebook contacts, heterogeneity of the personal network could be understood as an

indicator of diverse social communication, in other words, Facebook cosmopoliteness.

A personal network can be characterized as either cohesive or heterogeneous.

According to network theories, network cohesion and heterogeneity are the competing

concepts. The cohesive and integrated social network is advantageous in achieving

benefits of expressive action (Coleman, 1990). Borrowing from Heider’s balance theory,

Krackhardt (1992) also suggests the prominent role of affective ties, defined as “philos,”

in the situation of instability. Social capital theories argue that it is the cohesive nature of

social relations that returns collective benefits to the members of a community (Coleman,

1989).

Meanwhile, network cohesion is not a panacea. Cohesion sometimes causes

negative consequences, such as exclusion of others, too many demands of conformity and

reduced individual privacy and autonomy within a community (Portes, 1998; Simmel,

1955). Moreover, as Rogers and Kincaid (1981) and Burt (1992) argue, an open and

sparse network can be beneficial for instrumental actions because it embeds diverse

information. The benefits of an open network tend to be more like a private good

(Borgatti, Jones, & Everett, 1998). Burt (1992) argues that individuals benefit from

instrumental actions by having relational “holes” or disconnections in their personal

networks. Because dense social networks are more likely to circulate the same

information among the members, there is information redundancy. In contrast, if social

contacts are not connected to one another and are involved in different social clusters, an

actor has the advantage of accessing diverse and new information from direct and indirect

social contacts (Burt, 1992). Accordingly, less cohesive and more heterogeneous personal

52

networks may represent an ego’s cosmopoliteness, which leads the ego to gain access to

new resources such as “information outside immediate environment” (Summers, 1970,

p.178). Similarly, Burt (1999) discusses that opinion leaders are structurally positioned as

“brokers” who “trigger contagions across different social boundaries” and as “network

entrepreneurs” who maintain heterogeneous personal contacts (pp. 46-49). Drawing from

the network theories, I operationalize cosmopoliteness as personal network heterogeneity

and posit research questions as follows:

RQ1: Will Facebook cosmopoliteness, measured by personal network cohesion

and heterogeneity,be positively correlated with (a) observed opinion leadership and (b)?

5.3. Methods

Participants

The study is based on a field behavioral experiment in which recruited players

send recommendations to friends affiliated with the UB network. Specifically, 104

subjects sent messages on the first day of the experiment and 28 sent messages on the

second day. 4 missing cases were excluded, leaving a total of 128 opinion leaders for

further consideration.

Measures

Control Variables. Demographic variables including sex and school year were

included as control variables. Another important control variable is the number of

invitees to whom each opinion leader sent the message (simple network size) because it is

evident that the more people who are invited, the more will join the group.

Personality Strength. While there are various personality-related variables that

can be tested, Weimann’s PS index (Weimann, 1994) was adopted in this study. This

53

index integrates personality items relevant to defining the characteristics of the

influentials. The index is composed of 10 additive binary items. The possible score

ranges from 0 to 10. Table 3 includes the content of the index. Although the reliability

was not very high (α = .67), the scale was widely used and justified by previous literature

(see Scheufele & Shah, 2000).

Table 3. Personality Strength Index Items.

Items (Binary response: 1 = yes, 0 = no) I usually count on being successful in everything I do. I am rarely unsure about how I should behave. I like to assume responsibility. I like to take the lead when a group does things together. I enjoy convincing others of my opinions. I often notice that I serve as a model for others. I am often a step ahead of others. I own many things others envy me for. I often give others advice and suggestions.

Self-Designated Opinion Leadership. King’s and Summers’s (KS) opinion

leadership scale is applied to measure self-perceived opinion leadership. The KS scale is

the modified version from Rogers’ (1962/1995) scale in his original diffusion study. King

and Summers (1970) adapted this scale to explore whether opinion leadership is

generalizable across different topical or product categories. Accordingly, the strength of

the index is its flexibility for customization contingent on a product or an object of

54

interest. While Weimann’s scale (1994) delineates a general self-concept to characterize

the influential’s personality, the KS scale intends to measure self-evaluation as an

opinion leader on a specific topic. The KS scale is composed of 7 additive items. The

possible score ranges from 7 to 16, α = .75 (Table 4).

55

Table 4. King and Summers’ Opinion Leadership Scale.

KS Index Items (number in parenthesis is the score for each item) In general, do you like to talk about campus community issues* with your friends? (No =1, Yes =2) Would you say you give very little information (1), an average amount of information (2), or a great deal of information about campus community issues to your friends (3)? During the past six months, have you told anyone about some campus community issues? (No=1, Yes =2) Compared with your circle of friends, are you less likely (1), about as likely (2), or more likely (3) to be asked for advice about campus community issues? If you and your friends were to discuss about campus community issues, what part would you be most likely to play? Would you mainly listen to your friends' ideas (1) or would you try to convince them of your ideas (2)? Which of these happens more often? Do your friends tell you (1) or do you tell them about campus community issues (2) ? Do you have the feeling that you are generally regarded by your friends and neighbors as a good source of advice about campus community issues? (No = 1, Yes =2) * Examples are provided as follows: tuition, policies and regulations, budget allocation, events, facility improvement, quality of student services, advocacy actions led by administrators, etc.

Observed Opinion Leadership. To produce this variable, a researcher matched

the membership list with invitee lists acquired from each player, inducing the number of

members mobilized by each player’s invitations sent through the player’s Facebook

personal network. The severe skew of the variable, with a range from 1 to 75 friends

mobilized, raises the issue of normality of dependent variables for linear regression

modeling (Trabachnick & Fidell, 1996). Accordingly, I transformed the raw scores into a

56

seven-point scale, meeting the normality assumption (1~5 transformed to 1, 6~10 to 2,

11~15 to 3, 16~20 to 4, 21~25 to 5, 26~30 to 6, more than 30 to 7).

Facebook Gregariousness and Social Activities. Items for social attributes are

collected by asking players questions about their social communication and by collecting

data about social activities recorded in each player’s Facebook profile. There are no

previously devised scales for this variable in the Facebook context. Therefore, I came up

with the scales as follows.

First, I devised survey items by modifying offline questionnaires about social

attributes, partly adapting from Summers (1970). Specifically, I asked subjects their

perceived self-popularity (“How popular do you think you are on Facebook? (1)”),

interpersonal interactions through profiles (“Among various activities you can do on

Facebook, how important it is to look at others’ profiles? (2)” “Among various activities

you can do on Facebook, what proportions are made up of leaving your traces at your

friends' profiles, for example, commenting on their walls or pictures, poking, and posting

likings? (3)”), the size of recent communication partners on Facebook (“Among those

who are listed as friends in your profile, with how many friends have you communicated

through Facebook during the last 7 days? (4)”) and the intensity of Facebook use (“On

average, how many hours do you use Facebook per day? (5)”). All questions were asked

on 7-point scale items.

Second, I retrieved information from subjects’ profiles about their actual

participation in Facebook groups, including the total number of Facebook groups of

which they are currently members (6) and the number of issue-relevant groups that are

among those they joined (i.e. Facebook groups sponsored by student government or

57

advocacy organizations) (7). The number frequencies were converted into 7-point scales

to make them compatible with the scales used in the first part.

A principal component analysis with varimax-rotated factor solution was

employed to explore whether the seven items comprised different dimensions of social

attributes. The factor solution indicated two distinctive factors whose eigenvalue loadings

were greater than 1. The results showed that items related with group activities, the size

of communication, and Facebook use were bundled in the first factor and the items

related with interpersonal interactions and popularity were in the second factor. The first

factor was then defined as Facebook social activities and the second as Facebook

gregariousness. The inter-item reliabilities were not very high; however: α = .53, α = .59

respectively. Two factors explained 49.76 percent of the total variance (Table 5).

Table 5. Factor Loading of Facebook Social Attributes

Items Component

1 2 Membership in FB group (General)

0.73 0.04

Membership in FB group (Student organization)

0.72 -0.23

Number of friends recently communicated in FB

0.67 0.09

Facebook use intensity 0.54 0.30 Interaction through FB profiles 0.16 0.82 Checking friends' FB profiles -0.28 0.68 Popularity 0.13 0.54 Eigenvalues 1.91 1.57 % of total variance explained 27.29 22.47

58

Network-based Measure of Facebook Cosmopoliteness. Out of 128 opinion

leaders, 72 allowed me to use their social network data. Accordingly, cosmopoliteness

was measured with 72 respondents’ personal networks.

Three network properties are used as the indicators of network cohesiveness or

heterogeneity: density (Wasserman & Faust, 1994), the clustering coefficient (Watts &

Strogatz, 1998), and the number of different subgroups occurring by community structure

analysis based on the Girvan-Newman algorithm (Girvan & Newman, 2002). The details

of these properties are explained based on graph theoretic formalization. To do so, I use

the term “vertex” to denote each Facebook friend identified within an opinion leader’s

personal network and “edge” to refer to the relational tie in a pair of Facebook friends.

Accordingly, an opinion leader’s personal network is defined as G = (V, E), consisting of

a set of vertices v and a set of edges e.

First, density is defined as the proportion of existing edges to the maximum

number of edges that can exist in a network. Figure 5 shows examples of two opinion

leaders’ personal networks which both consist of 140 individuals. Even though the

network sizes are the same, the number of existing edges is different: the network

visualized in Figure 5a has 1,786 edges with a density of 0.09, while the network

visualized in Figure 5b presents only 838 edges with a density of 0.04.

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Figure 5. Ego-networks of Two Recruited Inviters, with the Same Size but Different Density (Ego is not visualized).

(a) Network size N =140; Number of edges =1886; Density = .09 (b) Network size N = 140; Number of edges =838; Density = .04

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Density indicates the extent to which friends identified in an opinion leader’s

Facebook profile are also listed as friends in each other’s profiles, meaning that they are

known to one another. Accordingly, when the network size is held constant, high density

implies that friends are interconnected to one another within a network, thus more

cohesive. In reverse, low density means that friends are not acquainted with one another

and likely to be known to the ego in different social contexts. To formalize, density D is

= ∑ ∑ ij

m: , ∈ ij ∈ , (1a)

where eij is the tie between vertices i and j and Em is the maximum number of ties

possibly existing, which is calculated as n(n-1)/2 for the network of size n. Accordingly,

(1a) is re-written as = ∑ ij( ) (1b)

Second, a clustering coefficient indicates the extent to which acquainted friends

share mutual friends. The rationale that the clustering coefficient is an indicator of

network cohesion is derived from Simmel’s discussion of triadic relationships (1950). In

a triadic relationship where two actors have a mutual friend between them, the relational

rule is applied in a qualitatively different way from a dyadic relationship. In a dyadic

relationship, there is autonomy for an actor to make a relational decision because both

parties equally rely on each other. In a triadic relationship, however, autonomy is greatly

reduced in that even though one actor is not compliant to the partner, the partner still can

rely on the third party. Accordingly, a triadic relationship embeds greater social influence

toward conformity than the dyadic relationship. In addition, Granovetter (1982) argues

that the completely connected triangle among three actors is likely to occur when their

relationships are based on strong ties. With the triad as a unit of analysis, a traditional

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clustering coefficient (Luce & Perry, 1949; Wasserman & Faust, 1994) is defined as the

following:

ˊ = ( ) (2)

In a sense, Cˊ is understood as the density of vertices that compose triads. In this

study, the calculation of a clustering coefficient is based on the extended version of (2)

introduced by Watts and Strogatz (1998). It begins by defining a local clustering

coefficient, which is the density of a sub-graph Ni = (V', E'), consisting of a set of

neighboring vertices that are directly connected to the vertex i and the subsequent edges.

Formally, the nlocal clustering coefficient of vertex i is = ∑ ij( ): ∈ ′ ij ∈ ′ (2a)

where k is the size of Ni. Based on Watts and Strogatz (1998), the clustering coefficient

of an opinion leader’s personal network G, denoted as CG, is calculated by averaging

local clustering coefficients of all vertices in G. That is,

G = ∑ (2b)

Next, the number of subgraphs in an opinion leader’s personal network is used as

an indicator of heterogeneity. The number of subgraphs is computed with a Girvan-

Newman algorithm. The Girvan-Newman algorithm utilizes the measure of “edge-

betweenness” to detect the sub-structures of a network (Girvan & Newman, 2002,

p.7822). Edge-betweenness is analogous to Freeman’s (1978) betweenness-centrality of a

vertex. Specifically, edge-betweenness is defined as the number of the shortest paths

between pairs of vertices that run along the edge of interest. Equivalent to the vertex

betweenness, if there is more than one shortest path between a pair of vertices, each path

is given equal weight. The betweenness of edge k in G(V, E) is formalized as

62

BE(k) = ∑ ij( k)ij

, , ∈ , ∈ (3)

where Sij is the number of shortest paths from i to j, and Sij(Ek) is the number of shortest

paths from i to j that pass through an edge k.

Edge-betweenness is used to find cut-edges that should be removed to break a

network into sub-networks. According to Girvan and Newman (2002), cut-edges must

have high edge-betweenness because “if a network contains groups that are only loosely

connected by a few intergroup edges, then all shortest paths between different

communities must go along one of these few edges” (Girvan & Newman, 2002, p. 7822).

By removing cut-edges, sub-groups are separated from one another, revealing the

underlying community structure. The Girvan-Newman algorithm is processed by three

steps: (1) calculating edge-betweenness of all vertices; (2) removing the edge of the

highest betweenness; (3) recalculating edge-betweenness of the remaining vertices; and

(4) repeating from (2) until no edge remains (Girvan & Newman, 2002).

Applying this logic to Facebook personal networks, I assume that subgroups

found by the Girvan-Newman algorithm should have been formed in distinctive social

contexts. For example, if an individual is a member of a Chinese student club as well as a

member of the Catholic campus ministry, friends from the Chinese student club must

know one another will “friend” each other on Facebook, as do friends from the Catholic

ministry. However, only a few friends from the Chinese student club might know anyone

from the Catholic ministry and vice versa. Accordingly, there are only a few ties bridging

the two groups. The more sub-groups are identified by removing such bridging ties

within the personal network, the more the ego is likely to have diversified social

relationships from different backgrounds.

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5.4. Results

Descriptive Analysis and Correlation Test. Among 128 opinion leaders, 55.8

percent of respondents were females (N = 72) and 43.4 percent were males (N = 56). (SD

= 6.09). Opinion leaders have attended the university for 2.02 years on average (SD

= .93). The average number of invitees was 59.63, although the variability was large (SD

= 56.10). Among them, 23.51 friends were mobilized for the advocacy group per person

(SD = 15.33). To make the outcome variable normally distributed, the number of

mobilized invitees was transformed into a 7-point scale, resulting in a mean score of 2.81

(SD = 1.49). In terms of self-designated opinion leadership, opinion leaders scored 7.05

out of 10 on average (SD = 2.17) for the personality strength and11.30 out of 16 on

average (SD = 2.31) for KS opinion leadership. The average score for Facebook

gregariousness was 4.10 out of 7 (SD = 1.10) and for social activity 3.67 (SD = 1.18).

Correlation analysis revealed that larger friendship networks are correlated with

having been in school longer (r = .20, p < .05), being higher in gregariousness (r = .19, p

< .05), and more active in social activity participation (r = .28, p < .01). Personality trait

showed significant correlation with both opinion leadership measures, KS index-based

measure (r = .29, p < .01) and observation-based measure (r = .19, p < .05). However,

there was no correlation revealed between the two opinion leadership measures (r = .09, p

= n.s.). Accordingly, Hypothesis 1 is not supported. Descriptive statistics and zero-order

correlation among these variables are found in Table 6.

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Table 6. Means, Standard Deviations and Correlations of Variables (N = 128).

1 2 3 4 5 6 7 8

1 SEX -

2 School YR -0.11 -

3 Gregarious 0.14 -0.10 -

4 Social Activity 0.05 -0.10 0.03 -

5 UB Friends

0.13 .20* .19* .28** -

6 Weimann Scale

0.09 0.00 0.17 -0.04 0.05 -

7 KS Scale -0.12 0.09 0.11 0.01 0.06 .29** -

8 Recruited Friends 0.09 0.08 .25** .38*** .49*** .19* 0.09 -

Mean 0.56 2.02 4.10 3.68 59.63 7.05 11.30 2.81 (SD) (0.50) (0.93) (1.10) (1.18) (56.10) (2.17) (2.31) (1.49)

Note: *p < .05. **p < .01. ***p < .001.

Opinion Leadership and Social Attributes. To test Hypotheses 2, 3, and 4,

hierarchical regression analyses were performed with two criterion variables, one of

which is the KS index based self-designated opinion leadership and the other

observation-based opinion leadership measured by counting the number of mobilized

friends. In both analyses, the control variables including gender and school year were

entered into the first block. The size of total UB friends was included in the second block.

All the missing variables were excluded list-wise.

The multiple regression analysis indicated that the overall model with self-

designated opinion leadership as a dependent variable was marginally non-significant, F

(6, 107) = 2.109, p = .06, only accounting for small variances, adjusted R2 = .06. The

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only significant predictor of self-designated opinion leadership was the PS index, another

self-perception based variable (β = .31, p < .001). Accordingly, Hypothesis 2a was

supported, while 3a, 4a, and 5a were not supported.

Table 7. Multiple Regression Analysis for Predicting Self-Perceived Opinion Leadership

Measured by King and Summers’ Index (N = 114).

B SE β t p

First block Gender -.33 .43 -.08 -.79 .432 School year .09 .18 .05 .50 .624 F (2, 111) = .48, p = .62, R2 = .09, adjusted R2 = .09 Second block Gender -.37 .43 -.08 -.86 .392 School year .06 .18 .03 .34 .737 UB friends .00 .00 .06 .64 .523 Fchange (1, 110) = .41, p = .52, R2

change < .01 Third Block Gender -.51 .41 -.11 -1.23 .221 School year .04 .17 .02 .22 .826 UB friends .00 .00 .07 .72 .475 Weimann** .32 .10 .31 3.36 .001 Fchange (1, 109) = 11.29, p < .01, R2

change = .09 Fourth Block Gender -.52 .42 -.12 -1.24 .218 School year .05 .18 .03 .28 .777 UB friends .00 .00 .06 .56 .579 Weimann** .32 .10 .31 3.32 .001 Gregarious .02 .07 .02 .22 .824 Social Activity .01 .05 .02 .25 .807 Fchange (2, 107) = 0.05, p = .95, R2

change < .01 The overall model: F (6, 107) = 2.109, p = 0.06, adjusted R2 = .06. Note: **p < .01; Missing cases were excluded list-wise.

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On the other hand, when predictors were regressed against the observed opinion

leadership as a dependent variable, the model showed a good model fit with much higher

accounted variances: F (6, 103) = 8.49, p < 0.001, adjusted R2 = .29.

When the control variables were entered into the first block, neither gender (β =

.09, n.s.), nor school year (β = .06, n.s.) were statistically significant. Unsurprisingly, the

number of friends on Facebook was positively associated with the number of recruited

friends, β = .48, p < .001, accounting for 22.4 percent of variance change.

When the personality trait variable was included in the third block, the variables

uniquely explained 4 percent of the variance in the observed opinion leadership

represented by the number of recruited friends, p < .05. In other words, Weimann’s PS

score was a significant predictor of the observed outcome of opinion leadership, β = .19,

p < .05, indicating that the higher a person was scored on the Personality Strength index,

the more friends the person could influence for the advocacy behavior. Therefore,

Hypothesis 2b was supported.

When two variables of Facebook social attributes were put into the model,

another 7 percent of variance was uniquely explained. Among the two social attributes,

gregariousness and social activity, only social activity showed a significant association

with observed leadership, supporting only Hypothesis 5b: For gregariousness, β = .16, p

= .09; for social activity, β = .23, p < .01. Among the three hypothesized predictors,

Facebook social activity was the strongest predictor of the opinion leader’s influence on

their friends’ advocacy behavior. Table 8 summarizes the results of regression model of

observed opinion leadership.

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Table 8. Multiple Regression Analysis for Predicting Observed Opinion Leadership

Measured by Behavioral Influence Outcome (N = 114).

B SE β t p

First block Gender .27 .29 .09 .94 .349 School year .08 .12 .06 .66 .511 F (2, 107) = .59, p = .55, R2 = .01, adjusted R2 < .01 Second block Gender .10 .26 .03 .38 .702 School year -.05 .11 -.04 -.49 .627 UB friends .01 .00 .48 5.45 .000 Fchange (1, 106) = 29.74, p < .001, R2

change = .22 Third Block Gender .04 .25 .01 .18 .864 School year -.06 .11 -.05 -.57 .573 UB friends*** .01 .00 .48 5.58 .000 Weimann** .13 .06 .19 2.28 .025 Fchange (1, 105) = 5.20, p < .05, R2

change = .04 Fourth Block Gender -.01 .25 -.00 - .04 .968 School year .01 .11 .00 .04 .965 UB friends*** .01 .00 .39 4.40 .000 Weimann** .13 .06 .19 2.37 .019 Gregarious .07 .04 .15 1.74 .085 Social Activity** .07 .03 .23 2.76 .007 Fchange (2, 103) = 5.15, p < .01, R2

change < .07 The overall model: F (6, 103) = 8.49, p < 0.001, adjusted R2 = .29. Note: *p < .05, ** p < .01, *** p < .001; Missing cases were excluded list-wise.

Personal Network Heterogeneity and Opinion Leadership. Network-related

variables were calculated with sociometric data of 72 opinion leaders. To do so, I used

the software ORA 2.0 developed by Carley (2009). The mean density for 73 personal

networks was 0.22 (SD = 0.13) and had a clustering coefficient of 0.53 (SD = 0.17). On

average, each personal network had 5.57 subgroups (SD = 3.61). Hierarchical regression

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modeling was not conducted due to small sample size. Instead, correlation tests were

conducted to explore research questions 1a and 1b.

The correlations between network variables and opinion leadership measures are

presented in Table 9. First, I tested the bivariate correlations. The results revealed that the

observed opinion leadership was negatively correlated with density (r = - .40, p < .01)

and positively correlated with the Girvan-Newman subgroup (r = .35, p < .01).

On the other hand, as seen in the table, observed opinion leadership was highly

correlated with personal network size. Considering the impact of the network size, I

additionally tested partial correlations with network size controlled. The pattern revealed

similar results, except for the relationship between the Girvan-Newman subgroup and

observed leadership whose significance was weeded out. Given that density is the

indicator of network cohesiveness and the Girvan-Newman subgroup measures’ network

heterogeneity, the results find positive correlations of network heterogeneity with

observed leadership and negative association with the cohesive personal network. On the

other hand, the clustering coefficient seems not an appropriate measure to operationalize

Facebook cosmopoliteness.

Meanwhile, Research Question 1b explores the relationship between self-

perceived opinion leadership and Facebook cosmopoliteness. The results showed that any

network-based measurement of cosmopoliteness did not show significant correlation with

the KS score.

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Table 9. Correlations between Network Variables and Opinion Leadership (N = 72).

1 2 3 4 5 Network Size

1 KS_partial - 0.066 KS_bivariate - 2 Observed_partial -.06 - 0.64*** Observed_bivariate .06 - 3 Density_partial .1 -.21* - -0.36** Density_bivariate .04 -.40** -

4 Clustering_partial .16 -.11 .68*** - -0.1 Clustering_bivariate .15 -.15 .67*** - 5 GN_partial .09 .11 -.47** -.33** - 0.36** GN_bivariate .13 .35** -.54*** -.35** - Mean 11.09 2.89 .21 .53 5.57 (SD) 2.24 1.51 .13 .17 3.61 Note: *p < .05. **p < .01. ***p < .001; KS = King and Summers’ scale, Observed = observed opinion leadership based on the recruited friends, GN = number of sub-groups created through Girvan-Newman algorithm

5.5. Conclusion and Discussions

Strategic communication planners have attempted to identify a subset of the

population who can influence people’s attitudes, opinions, and behaviors. Studies have

adopted four approaches – sociometric, self-designated, informant rating and

observation– to indentify opinion leaders and their role in the diffusion process of

innovation. Identifying opinion leaders and having them as early adopters is an important

task for marketers and campaigners to accelerate the diffusion process. In the context of

Web 2.0 in which the interpersonal visibility is greater than offline or traditional CMC,

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personal influence emerges as a even more crucial factor to expedite word-of-mouth

communication.

The current chapter conducted a cyber-field experiment by having subjects play

opinion leaders and by observing how they actually influenced their Facebook friends’

behaviors. The study employed two techniques, self-designated and observation, to

represent the degree of each player’s opinion leadership. By comparing the results from

the two techniques, I attempted to find a more valid indicator for the Facebook

influentials. Interestingly, the study found no correlation between the two methods.

Furthermore, the measurement of self-designated opinion leadership based on the KS

scale was not predicted by Facebook social characteristics, although it was significantly

associated with personality strength. Observation-based opinion leadership, on the other

hand, showed positive association with individual’s Facebook social activities and

cosmopoliteness as well as personality characteristics.

The incongruence between the two methods has several implications for future

online opinion leadership studies. First, it revisits the issue of false consensus (Rice &

Aydin, 1991), that is, the over-estimation of one’s ability to exert influence on others’

opinion or behavior. It is important to note that self-designation is based on self-

perception, not on the perspective of the influenced. Perceiving oneself as being

influential may not necessarily be linked to the actual ability to influence, at least in the

Facebook context. Although the self-designated technique might be a handy approach to

identify the influentials preliminarily thanks to its convenience, a researcher should be

aware that those scored high in self-designation will not always turn out to have many

followers in actual performance.

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Meanwhile, another possibility for the discrepant results is attributed to the

particular experimental context. The project intended to mobilize college students to

create a cyber-advocacy network. Because joining the network does not require much

commitment or cost, potential adopters might not engage in in-depth cognitive processing

for a cost-benefit assessment whether to do it or not (e.g. Clicking ‘join the group’ button

on Facebook requires definitely less cost than, say, purchasing an iPod). Accordingly,

potential adopters might not be very dependent on informational influence. Given that the

KS scale measures a person’s expertise or skill on a certain issue or object, it represents

opinion leadership based on the degree of informational influence. From this perspective,

it is not surprising that self-designated opinion leadership was not a valid indicator of the

influentials.

Cao, Knotts, Xu, and Chau (2009) discuss that there are two non-mutually

exclusive types of influentials: influencer and connector. An influencer exerts

informational influence through his or her knowledge and expertise. A connector has

many social connections and is thus capable of spreading a message to wider audiences.

The result of this project suggests that the influentials in this project turned out to be

more like connectors than influencers. This conclusion is evident in that the actual

capacity of changing others’ behavior was positively predicted by a person’s social

attributes reflected by his or her social networking practice on Facebook. Although not

hypothesized, Facebook personal network size, the simplest indicator how actively a

person engages in social networking with others, greatly contributed to the mobilization

outcome. In addition, the positive associations of the extent of group activities in which a

person participates on Facebook and the heterogeneity of the ego-network structure with

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the person’s recruitment capacity imply that social connectors are probably a more

important source of influence than knowledge-based influencers.

This result is tentative, however, given that the experimental design provoked a

non-profit and relatively simplistic behavior. To confirm whether the discrepancy

between self-designated and observed opinion leadership is attributed to the problem of

false consensus, to the experimental design, or to the particularity of the Web 2.0 context,

future research might be needed to apply the methods to other situations in which more

dynamic cognitive processing is required in adoption decision-making.

Another contribution of this chapter is that it introduces the network-based

measurement of cosmopoliteness. A physical mobility-based conceptualization of

cosmopoliteness is not well-suited in the online context where heterogeneous social

interactions from different cultural backgrounds are possible without necessarily moving

around. Taking advantage of the visibility of online social networks (Xu, Zhang, Xue, &

Yeo, 2008), I borrowed the Facebook network structure properties as parameters to

measure the degree of individuals’ cosmopoliteness. The rationale to use network

measures is based on the assumption that the cosmopolite person will maintain a more

heterogeneous personal network, which is structurally less dense and includes more sub-

groups. The result of a positive correlation of network heterogeneity with the observed

opinion leadership suggests that the network-based understanding of cosmopoliteness can

be a justifiable approach to measure one dimension of social characteristics of the

influentials. The structural approach to social characteristics, however, is a novel

approach in opinion leadership literature. Additional research in different contexts needs

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to be added to confirm that a network-based measurement is a proper approach to

characterize social aspects of online opinion leaders.

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VI. WORD-OF-MOUTH ON FACEBOOK: STRUCTURAL APPROACH

6.1. Structural Approach to WOM Effect on Social Organizing on Facebook

6.1.1. WOM Communication in Facebook

The Word-of-Mouth (WOM) communication strategy, which refers to the use of

informal interpersonal communication channels to promote products, brands or services

(Brooks, 1957), has been regarded as the most effective alternative to the traditional

forms of strategic communication (Trusov, Bucklin, & Pauwels, 2009). Traditionally,

WOM research relies on two approaches: self-reports on surveys, stemming from the

original research on interpersonal influence by Katz and Lazarsfeld (1955), and adoption

studies inspired by Coleman and his colleagues’ (1966) pioneering diffusion research.

Unfortunately, because neither approach could provide straightforward evidence of

WOM effectiveness, researchers could only infer the presence of WOM effects from the

data (Trusov et al., 2009).

During the past decades, digitally connected social networks through which

preexisting as well as newly formed relationships are maintained, enhanced, and

extended (Haythornthwaite, 2002; Wellman, Boase, & Chen, 2002) have been

particularly spotlighted as the amplifier of WOM processes with lower costs and fast

diffusion. Along with the buzz for the prospect of electronic word-of-mouth (e-WOM),

scholars have endeavored to supplement the scant evidence of WOM effects in the

traditional offline context by taking advantage of easily accessible online archives of

referral histories. Examples include usenet posts (e.g. Godes & Mazline, 2004), online

product reviews (e.g. Chevalier & Mayzlin, 2006; Liu, 2006; Mishne & Glance, 2006)

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and pass-along emailing (Norman & Russel, 2006; Phelps et al., 2005). Even those e-

WOM studies, however, are limited for two main reasons. (1) Except only a few cases of

e-WOM research (e.g. De Bruyn & Lilien, 2008; Trusov et al., 2009), literature are

predominantly predisposed to message senders (or transmitters) rather than the effect of

WOM on message recipients. (2) Discussions revolve exclusively for the sake of for-

profit marketing, while somewhat disregarding the implications of e-WOM on less

commercially driven areas such as social marketing, cause-related campaigns, or non-

profit advocacy.

In recent years, social networking sites, notably Facebook, have emerged as one

of the most successful venues for e-WOM. The so-called “social context ad” rises as a

novel advertising strategy, lending Facebook the selling power equivalent to major search

engines such as Google and Yahoo!, the forerunners of interactive advertising (Steel &

Fowler, July 7, 2010). Facebook’s social context advertising garners the spotlight from

marketers thanks to the full-fledged use of online connectedness for a viral effect.

Social context advertising is performed based on the software that Facebook

developers call “social plug-ins,” including “Like Buttons,” “Recommendations plug-in,”

“Login Button,” “Comments,” “Activity Feed,” “Like Box,” “Friendpile plug-in,” and

“Live Stream” (for description of each plug-in, see

http://developers.facebook.com/plugins). Once embedded in advertisers’ websites, social

plug-ins allow visitors to share their attitudes, thoughts or behaviors about products or

activities advertised on the websites with other friends on Facebook. The impact that the

rise of social context advertising has on the interactive advertising market is not trivial.

Fowler and Efrati (August 2, 2010) report that the implementation of social plug-ins

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results in “one- to five-fold increase in referral traffic from Facebook” (p. B1) and

visitors from Facebook stay on advertisers’ websites for 20 percent more time than

visitors from search engines.

Not just profit-oriented markets that can benefit from Facebook-like WOM

communication; non-commercial sectors benefit from e-WOM as well. Examples are

abundant. Thackeray, Neiger, Hanson, and McKenzie (2008) discuss the advantage of

using social networking sites or micro-blogging services to enhance personal

recommendations-based health promotions. On Facebook, for example, a Facebook page

of the American Public Health Association (APHA) is run solely for recruiting new

members to APHA and sharing information (Thackeray et al., 2008). Regarding the

impact on political campaigns, Williams and Gulati (2007) empirically examined the

effect of using Facebook social networks on political candidates’ actual voting shares.

Particularly, the implemented software “Election Pulse” on Facebook not only creates

candidates’ profiles but also helps supporters become easily informed about their

candidates’ updates, share their candidate preferences (who they “like”) with other less

political-minded friends, and connect themselves with other supporters. Williams and

Gulati’s 2007 results showed that successful utilization of Facebook contributed to

candidates winning a higher percentage of vote shares. Additional examples include

‘Support the Campaign for Cancer Research’ which has over 3 million members and has

raised nearly $60,000, ‘Stop Global Warming’ with 1.7 million members and $21,000 of

fundraising, and the successful activism group "One Million Voices against the FARC”

that drove 10 million protesters on the street world-wide. These examples suggest that

Facebook social networks prevalently play a role in social organizing and fundraising and

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contribute the spread of social consciousness and promote civic engagement, pro-social

behaviors and community participation (Maderazo, 2008; Neumayer & Raffl, 2008).

In sum, WOM communication on Facebook potentially benefits social marketers,

non-profit organizers and cause advocates as well as commercial marketers. E-WOM

research can take advantage of observable social network structures on Facebook to find

empirical evidence of WOM effects on message recipients’ attitude or behavior changes.

6.1.2. Facebook WOM as Social Influence Process: Structural Approach

WOM communication can be seen as a process of social influence. Various social

psychological motives are offered to explain how social influence occurs. One motivation

is the desire to seek informational accuracy: People try to perceive the state of reality

correctly to react properly to social situations they encounter (Cialdini & Goldstein,

2004). When there is uncertainty regarding perception, people compare their own

opinions, attitudes or beliefs to those that are held by others with whom they interact. As

a part of the continuous effort to reduce uncertainty, social comparisons induce

informational influence from reference groups (Festinger, 1954).

Second, social influence becomes normative when others’ attitudes or behaviors

are a role model that is desirable for an individual to conform. Normative influence

occurs when people are motivated to be affiliated with the reference group or to maintain

a positive self-concept within the group (Cialdini & Goldstein, 2004). If an individual

perceives that a certain belief or behavior is shared among those with whom the

individual wants to maintain meaningful relationships, the individual would be likely to

commit him or herself to the belief or behavior to be affiliated with the group of people

or to avoid social sanctions as the result of deviation. By expressing desirable opinions or

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attitudes, the individual will also gain social rewards such as popularity, affinity or a

reputation that increases his or her self-esteem and positive self-image (Visser & Mirabile,

2004).

Aligning with social influence literature in other topical areas (Rice, 1999;

Iyengar, Ban den Boulte, & Valente, 2010), the WOM process reveals two sources of

social influence: individual influence and structural influence. Individual factors refer to

variables such as personal expertise, skill, personality and psychological traits. Much of

the opinion leadership literature is exemplary in underscoring the contribution of such

individual traits on the information givers’ motivation and influence on others’ decision-

making. In this sense, the previous chapter can be considered as testing the individual

factor underlying the WOM communication.

On the other hand, social structural influence on WOM has been relatively less

studied. As Contractor, Seibold, and Hellor (1996) argue, “structures have no reality

independent of the interactions they constitute and in which they are constituted” (p. 458).

In other words, a meticulous investigation of structural factors requires a researcher to

know where to locate a WOM participant’s social position in relation with other

interactants. Given that the task to identify the social structural position of each

individual is hardly easy, a paucity of literature focusing on structural influence is not

surprising.

Nonetheless, structural factors are equivalently important to individual factors in

that our attitudes or behaviors cannot be but socially constructed (Fulk, 1993). The effect

of WOM communication also indebts itself to the message recipients’ socially

constructed perception toward the advocated product or service. In this sense, this

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chapter can contribute to the progress of the WOM literature by exploring structural

influence on the WOM processes occurring through Facebook informal social networks.

6.1.3. Theories of Structural Social Influence

In organizational theory, Salancik’s and Pfefffer’s (1978) “social information

processing model (SIP)” explains how an individual’s perception, attitude and behaviors

are influenced not just by objective attributes of the task and his or her personal traits but

also by the opinions, beliefs, and behaviors of “salient others” (Rice & Aydin, 1991,

p.220). Based on Festinger’s social comparison theory (1954), SIP proposes that

individuals are adaptive agents to their social contexts in which social information is

produced. Several sub-processes are stated to explain how social information affects

individuals’ attitude or behavior towards organizational tasks to which they are assigned

(Salancik and Pfefffer, 1978).

First, social information can be direct statements uttered by other organizational

members. When an individual is exposed to the other members’ overt evaluation about a

certain dimension of the job, the exposure to such social information will put the

individual in the situation in which he should align himself with the others by verbally

agreeing with the statement. According to Salancik and Pfeffer (1978), the verbal

agreement “may eventually convince the individual himself (p. 229)”.

Second, even though there are not direct statements articulated by others, the

repeated observation of others’ attitudes or behaviors can make a person perceive a

certain aspect of environment more prominently than before. The aspect of the

environment that the person has not been previously cognizant is recognized through the

repeated infusion of social information produced through social interactions. Not only

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does this information help the individual perceive certain aspects of the environment,

social information also makes him or her learn how to interpret the situation through

value-laden (positive and negative) evaluations. If a negative interpretation is assigned to

the issue, the individual finally learns what needs, values or requirements would make the

job environment better.

While the direct sub-process of social information produced through verbal

utterances is analogous to the solicitation effect on compliance behaviors, the indirect

sub-process of the repeated infusion of social information resonates with the network

exposure effects found in the threshold model of diffusion (Valente, 1995) and collective

behaviors (Granovetter, 1978). That is, the point to which the infusion of social

information eventually triggers the attitudinal or behavioral change can be understood as

a person’s threshold, which refers to the extent the person is exposed to others’ attitudes

or behaviors within his or her personal networks enough to be influenced by it

(Granovetter, 1978). Valente’s network model of diffusion (1995) conceptualizes social

influence from salient others’ behaviors as “personal network exposure (PNE)” (p. 43).

Figure 6 delineates how PNE is associated with the proportion of adopters among the

individuals with whom the individual interacts.

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Figure 6. An Example of Personal Network Exposure to Social Information

(a) social information about no one; PNE = 0

(b) social information about one person; PNE = 20%

(c) social information about three; PNE = 60%

(d) social information about four; PNE = 80% (If the actor follow salient others’ behavior at this time, the actor’s network threshold is 80%)

Note. Black dotted people are the ones of whom the actor is aware as an adopter of the attitude or behavior of interest.

Because an individual’s threshold is independent of PNE, equivalent exposure

rate does not lead to a homogeneous behavioral outcome (Valente, 1995; 2005): For

individuals with low threshold levels, even a small magnitude of social information could

trigger social influence. On the other hand, others may be resistant to social influence

even with much greater degree of PNE.

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One important issue arising with SIP theory is how to identify “socially relevant

others” who serve to influence individuals (Shaw, 1980, p. 45). Meyer (1994) and Rice

(1994) point out that the majority of SIP studies fail to specify who the socially relevant

others should be. According to Rice and Aydin (1991):

[Most] studies rely on a ‘generalized other’, where the ‘other’ does not refer to specified, named individuals in the local social context but to a general category, such as "coworker" or "best friends." The reliance on such generalized others makes it difficult to specify the exact source and mechanisms of the social information process. The use of generalized others also …assumes that the respondent can accurately estimate other's attitudes or behaviors. However, Rice and Mitchell (1973) found that there was no significant correlation between subjects' ratings and the ratings of the subject's coworkers of the extent of their collaboration or their social interaction (p. 221).

To overcome the operationalization problem, it is advantageous to incorporate a

network analytic approach to the social information processing model. As Pollock,

Whitbred, and Contractor (2000) note, adopting network analysis to social influence

studies can help overcome this operationalization problem. For example, Schmitz and

Fulk (1991) adopted an ego-network method, in which a subject is termed ego and a few

frequent communicators selected by each ego are termed alters. Schmitz and Fulk (1991)

quantified the degree of social information for each ego by averaging alters’ actual

evaluations about a product. Even with this specified technique, however, their approach

does not contain the full information about socially relevant others because alters were

arbitrarily chosen from within a fixed number of friends (six in Schmitz and Fulk’s study).

Rice and Aydin (1991) pointed out this limitation and introduced more rigorously

network-based measures of social proximity, emphasizing the importance of looking at

the multilevel structural context from dyadic relational strength to network positions and

to spatial proximity. Network analysis of social proximity was also used in predicting the

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effect of social influence on employee turnover behaviors (Feeley & Barnett, 1997;

Feeley, Hwang, & Barnett, 2008).

The network structural effect on individual’s susceptibility to social pressure to

conform has been theorized as social contagion process (Burt, 1987; Burt & Janicik, 1995;

Hartman & Johnson, 1989). Social influence psychologists distinguish the contagion

process from social facilitation or compliance process, defining contagion as “an event in

which a recipient’s behavior has changed to become ‘more like’ that of the actor or

imitator. This change has occurred in a social interaction in which the actor has not

intentionally communicated intent to evoke such a change” (Polansky, Lippit, & Redl,

1950, p. 322).

Social contagion theory has been applied predominantly in an organizational

context, given the relative easiness of explicating positional structure within definite

organizational boundaries. Social contagion theory aligns with SIP in that it also argues

that organizational behavior does not arise free from social structure. Meyer (1994)

highlights the similarity of the two and proposes the incorporation of social contagion

measures into the SIP model. He specifies that contagion occurs through three

mechanisms: simple direct contact, cohesion or group affiliation and structural

equivalence mechanism (1994). Figure 7 describes how the three mechanisms are

structurally different.

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Figure 7. Simple Representation of Three Social Contagion Mechanisms2

Simple direct contact is the most parsimonious description of the SIP process: The

interpersonal interaction will increase the perceptual or behavioral similarities among the

social contacts. Accordingly, frequency of contacts is regarded as important; repeated or

multiple direct contacts will increase social pressure to conform. On the other hand, the

simple direct contact mechanism is simplistic in that (a) it only “assumes nothing more

about the relationship with comparison others than that they interact directly with one

another” and (b) it only “implies an inherently dyadic perspective” rather than triadic or

higher-order relational structure (Meyer, 1994, p. 1021).

The second mechanism is cohesion (Burt, 1987; Marsden & Friedkin, 1993;

Meyer, 1994). Cohesion is conceptualized as the influence occurring through “frequent

and empathetic communication” that increases interpersonal attachment (Hartman &

Johnson, 1989, p. 524). Interpersonal attachment triggers the attitudinal, belief or

behavioral congruence among the actors. Cohesion can be measured in different ways: 2 From “Social information processing and social networks: A test of social influence mechanisms,” by G. W. Meyer, 1994, Human Relations, 47(9), p.1024, Copyright 1994 by The Tavistock Institute. Adapted with permission of the author.

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one, measuring tie strengths or communication frequency on a dyadic level; two, a

psychological assessment about over-socialization toward a designated group; and three,

structural analysis of “cohesive subgroups” (Wasserman & Faust, 1994, p.249).

Structural social influence highlights the third approach. When the third approach is

adopted, one can reveal unique structural properties of a network and an individual: At

the network level, the cohesive network reveals integrated connections among the

members; on an individual level, an individual’s embeddedness within the network can

be measured and used as a predictor of his or her social behavior (Moody & White, 2003).

Although three methods show different operationalizations, they are closely

related to one another. Specifically, when two actors are strongly tied, they are likely to

share mutual friends, forming higher-order interconnections together (Granovetter, 1973;

Simmel, 1954). According to Coleman (1989; 1990) and Granovetter (1985), members

embedded in such a dense network are likely to perceive a strong sense of emotional

attachment to the group and undergo an enhanced socialization process by sharing social

support and by building trust in the group. At the same time, strong group affiliation also

increases informal surveillance, privacy invasion, and a sense of obligation to group and

social pressure toward conformity, reducing autonomous behaviors. In other words, the

cohesion effect on social influence emerges under the mechanism of group affiliation.

Unlike direct contact or the cohesion mechanism, the structural equivalence

mechanism does not assert that direct social contact among members has to be a

prerequisite for social influence to occur. Instead, it emphasizes that the positional

similarity between actors results in attitudinal or behavioral similarity, even in the

absence of direct interaction with each other. The extent of positional similarity depends

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on the identical relational patterns through which actors receive similar information. Burt

(1987) argues that actors who are in the same position in a social system are likely to be

in a competing relationship in which actors will monitor each other through a third party,

and thus are aware of each other’s attitude or behavior. The structural equivalence

mechanism provides an alternative perspective to interaction-based mechanisms,

suggesting that the similarity in relational pattern can determine the socialization process

as well as the existence of direct contacts (Burt, 1987).

Conventional understanding is that, by cohesion, contagion occurs between

individuals in the same primary group, and by structural equivalence, contagion occurs

between competitors (Burt & Janicik, 1995). Although cohesion and structural

equivalence are conceptually independent mechanisms (Marsden & Friedkin, 1993), they

are not in opposition to each other. While cohesion and structural equivalence are often

proposed as competing theoretical explanations (e.g. Hartman & Johnston, 1989; Meyer,

1994), real social networks often reveal that they are not always contrasting to each other.

As seen in Figure 8, Figures A and B exemplify situations where cohesion and

structure equivalence compete with each other: In A, the cohesion effect is reduced

because of unequal positional structure; in B, contagion occurs not through direct

interaction with each other but through structural equivalence in which the third party

provides both actors with the same information. As presented in Figure C, however,

certain social structures lead to identical understanding of both concepts. Figure C

resonates with Simmel (1954) and Granovetter (1973) that interpersonal attachment

emerges from integrated social structures: The network of cohesive triadic relationships

creates the complete network as seen in Figure C. In a complete network, actors not only

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directly communicate to one another but also are positioned in a way that they are

structurally equivalent because their relational patterns are identical. In this sense, the

network in Figure C suggests that structural measures of cohesion be regarded as a

special case of structural equivalence, making identical predictions of contagion

outcomes between the two mechanisms.

Figure 8. Social Structures of Structural Equivalence and Cohesion 3

3 From “Social contagion and innovation: Cohesion versus structural equivalence,” by R. S. Burt, 1987, The American journal of Sociology, 92(6), p.1292. Copyright 1987 by The University of Chicago. Adapted with permission of the author.

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6.1.4. Typology of Structural Influence Mechanisms for Facebook WOM: Personal

recommendation, social contagion, and embeddedness

The SIP and social contagion theories were developed in a formal organizational

context. The theories, however, are also applicable to Facebook’s WOM-based social

organizing phenomena. Many social organizing practices on Facebook intend to achieve

instrumental goals such as mobilizing collective action, promoting advocacy, raising

donations, or social problem-solving. The formation and development of such groups can

be understood as an organizational behavior composed of micro-level decision-making

enacted by group members.

In the initial stage of group formation, potential members may encounter an

uncertainty to some extent in deciding whether to join the group or not. Various questions

may arise, such as whether the advocated issue is a problem worthy of their own effort, to

what extent the organizational effectiveness is expected, whether their role as a group

member is well-defined, and in what way the group activity will contribute to real life. To

reduce uncertainties, they will observe their Facebook friends’ behaviors. If they find that

their friends react to the group favorably, they are likely to perceive that the issue

promoted by the group is important enough to be assessed. Furthermore, if there are more

friends who show positive attitudes or behavior toward the group, the person will be

more convinced about the value of organizational action and are likely to become a

member.

Based on the SIP and social contagion theories discussed above, I propose a

typology of structural mechanisms underlying how WOM communication leads to the

group formation in Facebook. The first mechanism is the direct personal

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recommendation effect. This mechanism is analogous to the sub-process with direct

statements uttered by other organizational members in the SIP model or simple direct

contact mechanism explained by Meyer (1994). This mechanism is the most

straightforward influence of WOM communication. Simply speaking, the more

recommendations a person receives, the more social pressure he or she will perceive and

will thus be likely to comply with the recommendation. Based on the first mechanism,

H1: The more Facebook friends who make a personal recommendation about the

Facebook group to an individual, the stronger social influence the individual will

experience such that the individual will be more likely to comply with the

recommendation.

The second mechanism is the contagion effect. Individuals are exposed to social

information not only by receiving direct recommendations, but also by roaming

interpersonal networks on Facebook. Facebook interpersonal networks are the major

venue for Facebook WOM communication in that it facilitates friends’ behavioral or

attitudinal updates. In other words, Facebook social networks enable users to observe and

learn about others’ thoughts and activities through mundane social contacts, which should

produce more or fewer contagion effects. Likewise, similar to the first mechanism, the

second mechanism is also based on the influence occurring from direct social interaction.

It is differentiated, however, from the direct contact mechanism in that the influence from

this mechanism is unintentional and based on learning or imitation rather than

compliance with recommendations.

Wheeler (1966) states that the probability of contagion occurrence should increase

as psychological barriers, such as perceived cost, uncertainty toward the usefulness,

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boredom, or lame motivation, get low. For example, a lower degree of prohibition of an

action is more likely to lead to a person’s deviation when he or she is exposed to others’

deviation than a stricter degree of prohibition. The inherent conflicts whether to perform

online activity is usually set at a lower level than that of offline actions, because adoption

of a certain online activity tends not to demand substantial effort, cost and commitment,

or any harm to others.

Decision-making regarding online behaviors is generally based on lower

psychological barriers than offline. Likewise, the exposure to others’ behavior on

Facebook could easily resolve an actor’s internal conflict and motivate a potential actor

to go along with the advocated behavior. Based on the social contagion mechanism,

H2. Higher Facebook network exposure to the behavior will lead to a greater

social contagion effect such that an individual with high exposure to others involving in

the advocated Facebook group activity will be more likely to get involved in the group.

Meanwhile, there are two distinctions between Facebook groups and conventional

formal organizations that should be considered prior to explicating the third mechanism

of Facebook WOM. First, the organizational or group behaviors in Facebook are initiated

and developed through loosely connected informal friendship networks that present the

absence or minimal number of hierarchical superior-subordinate relationships.

Hierarchical authority has been an important factor that causes compliance and

conformity behaviors (Marsden & Friedkin, 1993). On Facebook, however, the authority

effect may be considered as a less explicit source of influence. Even if the boss of a

company is on a Facebook personal network, his authority can be exerted only implicitly

at best rather than directly affecting the activities emerging on Facebook, because the

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roles of boss and subordinate in the workplace is not expected on Facebook, the informal

social space, anymore. Therefore, vertical relationships are less visible on Facebook in

comparison with the formal organizational context.

Second, conventional organizational behaviors assume intra- or inter-

organizational competitions. Accordingly, the network advantage that boosts

entrepreneurship is often emphasized in the social contagion process. Particularly, Burt

(1987) argues that actors who are in structurally equivalent positions tend to get access to

similar resources and play common roles assigned to the position. Positional similarity

would lead actors to observe and imitate one another in an attempt not to lag behind.

Such competition induced from structural equivalence is much less likely to exist in the

process of Facebook WOM in that their influence networks are primarily composed of

friendship-based affective ties. Of course, some relationships on Facebook might have a

stake in offline social contexts, for example being coworkers in the same company. The

interests underlying offline social contexts, however, do not directly intervene in the

process of social organizing on Facebook because the causes and motivations pursued by

most Facebook activities are independent from formal organizational situations.

Particularly, the influence from structural equivalence should be even more diluted when

it comes to college students’ social networks whose predominant proportions are

composed of primary affective ties rather than stakeholder relationships.

The structural equivalence effect may function not only in hierarchical or

competitive relationships but also many other social contexts. Feeley and Barnett (1996),

for example, discuss that employees’ turnover is affected by structural equivalence

depending on who people communicate with. While hierarchical authority and

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competition might be minimally presented, the structural equivalence effect can exert

influence on Facebook. For example, it is possible that if certain information is shared by

A to B and C, the information helps B and C share the same attitude, even if B and C do

not directly communicate with each other. The structural equivalence mechanism,

however, is not explicated for further theoretical discussion in this paper due to the

computational difficulty with the vast network size. Nevertheless, one cannot assert that

the non-inclusion of this mechanism in the model neglects the implication of positional

characteristics on the social influence process. Although the proposed typology will not

convey additional theoretical discussions on structural equivalence, the prediction from

positional similarity will still be retained by considering structural cohesion. As stated

earlier, structural cohesion results in the same prediction of structural equivalence when

the operationalization is based on a complete network.

Structural cohesion is an important structural characteristic for effective WOM

because it determines an individual’s level of embeddedness within a network, which

refers to the third mechanism. According to Granovetter (1985), an individual’s social

action is coordinated within social networks. The more embedded an individual is within

a network, the less autonomy he or she maintains in decision-making because self-

interests are more dynamically interwoven with other members through the accumulated

social exchanges over time. Given that embeddeness implies the intensity and range of

social interactions, the extent of embeddedness on Facebook social networks can be the

indicator of the degree of social proximity an individual has with the others in a network.

If an individual is more strongly embedded in a network, the individual may be more

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strongly affected by social proximity, and thus perceive a greater social pressure to adopt

what others do. Based on the embeddedness mechanism,

H3. Higher embeddedness will cause greater social pressure such that the

individual will be more likely to become involved in Facebook group activities than less

embedded individuals will.

Finally, these three mechanisms may reveal interaction effects on an individual’s

decision making. One possible direction is the synergy effects. For example, if an

individual received a personal recommendation about a group from multiple friends and

also observed that many of his or her network friends also support the group, this direct

recommendation and contagion can produce a synergic effect to motivate the individual

to be a part of the group more intensively than either of the two mechanisms alone.

Likewise, if a person is deeply embedded in a network and observes that many others

follow the recommendation, the person’s stronger sense of group affiliation may intensify

the contagion effect from observing others’ behavior.

Alternatively, different scenarios can hypothesize compensatory interaction

effects. For example, social contagion may be helpful to facilitate WOM outcomes only

when there is no personal recommendation effect found, or vice versa. Given the possible

scenarios about the interaction effect among the mechanisms, the existence of interaction

effects among the three mechanisms are hypothesized.

H4: There will be interaction effects between (a) the frequency of direct

recommendations and the degree of social contagion, (b) the frequency of direct

recommendation and an individual’s embeddedness in a network, (c) the degree of social

contagion and an individual’s embeddedness in a network.

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RQ1: If any significant interaction effect is found, is the interaction pattern

synergic or compensatory?

6.3. Methods

6.3.1. Procedure

When the experiment was designed, I assumed that the ego networks would be

independent from each other. However, in reality each ego’s personal networks

overlapped in a non-trivial manner. Specifically, out of 72 egos, 56 turned out to be

identified as a friend of at least one of the other egos. Furthermore, 911 alters repeatedly

appeared across different personal networks. Figure 9 compares the real structure of

overlapping ego-networks with the independent personal network structures in theory.

A non-negligible portion of network overlap could mislead positional properties

of each alter if not given proper consideration. As exemplified in Figure 7, if personal

networks were treated as if they were perfectly independent of one another in contrast to

the real structure including overlaps, vertex A’s degree centrality would fail to be

properly calculated because the degree centrality would be differently defined depending

on which personal network vertex A is considered to be nested in. More importantly, an

alter’s multiple occurrences across different ego networks imply that the alter actually

receives the invitation message multiple times from different egos.

The solution for properly considering the network overlap is to aggregate all 72

personal networks into a system-level network and use the union network for analysis. In

the process of aggregation, I removed 6 non-human vertices, including “Buffalo Hillel,”

“Buffalo RHA,” “Schussmeister Skiclub,” “Take Fresh Galaxy Molson,” “UBOAC” and

“UB Rock Climbing.” Ten were also removed due to their failure to match the names

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with other information. After calculating the relevant structural measures, egos were

removed from the further inferential tests simply because they were not the samples to be

observed. To conclude, a total of 3,971 invitees were put into the statistical models.

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Figure 9. Presumed and Real Structure of Facebook Personal Networks

(a) Presumed structure of Facebook social networks, each of whose ego-networks is independent to each other

(b) Real structure of Facebook social networks. The vertex A’s degrees vary depending on where A is considered to be nested (1, if A is nested in Egonet 1, 3 in Egonet 2 and 5 in the whole network). Note. : edges connecting egos : edges connecting between alters

: edges hidden until the networks are aggregated.

: e

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6.3.2. Network Measures

Direct Personal Recommendations. The frequency of personal recommendations

an individual received was computed by simply counting the number of invitation

message senders (egos) who directly contacted the individual. Formally stated, direct

personal recommendations an individual i receives, Di, is defined as

D = ∑ (4)

where Aij is the adjacency matrix in which the cell aij is 1 if i and j are friend with each

other on Facebook and 0 if not, and Lj the column vector indicating whether j is a

message sender (1 if message sender, 0 if not).

Social Contagion Effect. Social contagion occurs through the exposure to the

networked others. Accordingly, I borrowed the measure of PNE from the diffusion

literature as a proxy of the contagion effect (Valente, 1995). PNE refers to the degree to

which an individual witnesses others’ adoption behaviors within his or her personal

network. To formalize the variable personal network exposure PNEi for an individual i,

= ∑ - ∑j (5)

where, Aij is the adjacency matrix mentioned in (4) and Mj is the column vector indicating

whether j is a member of the Facebook group. Di is subtracted, because direct

recommenders are not the source of contagion. As evident in (5), PNE ranges from 0 to 1,

expressed as a proportion of the enacted friends to the degree of all connections an

individual has.

Embeddedness. To examine the effect of structural cohesion, embeddedness was

operationalized as follows. Specifically, an individual is understood as being more

embedded in personal community depending on the extent to which an individual is

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included in a cohesive subgroup. I utilize the concept of Krackhardt’s simmelian-ties

(1998) to define a cohesive subgroup. According to Krackhardt (1998), the formal

definition of Simmel’s triadic relationship (1950) and Luce and Perry’s concept of the

“clique” (1940) share a great similarity. Specifically, Krackhardt (1998) operationalizes

that two actors are simmelian-tied to each other if they are co-clique members. Therefore,

if an individual is a co-clique member more frequently, he or she is understood as having

more integrated relationships with other network members. Formally, the clique matrix

C is,

= 1, ℎ 0, (6a)

where C is a two-mode network. Multiplication of C with the transposed form Cˊ results

in K = ˊ (6b)

where K is the co-clique matrix whose off-diagonal value indicates the number of cliques

two individuals share. The row sum of K (excluding the diagonal value) is the total

frequency of an individual’s being in co-cliques.

I assume that the increment of the embeddedness effect decreases as the level of

embeddedness becomes larger. For example, the difference between a person with 150

simmelian ties and a person with 151 simmelian ties will be much smaller than the

difference between a person with no simmelian tie at all and a person with one simmelian

tie. Considering the reduced incremental rate as the embeddedness level increases, I

propose to log-transform the row sum of K to quantify the score of embeddedness of each

individual. Therefore, the embeddedness of an individual i, Ei, is

i = ln (∑ ij + 1), ≠ j (6c)

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where 1 is added for cases ∑ ij = 0.

6.4. Results

6.4.1. Descriptive Analysis.

The union network aggregates 72 recruited egos’ personal networks. The sizes of

the ego network varied a great deal, ranging from 6 to 222. As mentioned earlier, after

the computation of network properties, egos were removed from further statistical tests.

Accordingly, 3,971 invitees were considered. The software ORA 2.0 (Carley, 2010) and

UCINET 6 (Borgatti, Everett, & Freeman, 2002) were used to create the system-level

network and to calculate the subsequent network variables.

Among the 3,971 invitees, a total of 883 supported the advocacy by joining the

group (22.2 percent). A single inviter (or ego) contacted the majority of invitees (N =

3,060, 77.1 percent). Among 22.9 percent invitees who received multiple invitations, 648

(16.3 percent) received the invitations from two inviters, 194 (4.9 percent) from three, 51

(1.35 percent) from four, 12 (0.3 percent) from five and 6 (0.1 percent) from more than

six inviters (M = 1.32, SD = 0.68).

Beside the direct personal recommendation received from egos, 63 percent of the

invitees were exposed to other invitees who became members of the advocacy group.

Specifically, 554 invitees (14 percent) were connected to one of the supporting invitees,

356 (9 percent) invitees to two, 316 (8 percent) to three, 251 (6.3 percent) to four, 181

(4.6 percent) to five and 843 (21.1 percent) to more than five supporters. On average, an

invitee was exposed to 3.39 friends who turned out to be a supporter of the group. As a

proportionate, the mean score of PNE was .12 (SD = .13), indicating that, on average, 12

percent of the invitee’s social contacts became group members.

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To compute embeddedness, simmelian ties were first counted for each invitee. 14%

of invitees (N = 556) were not simmelian tied to anybody. For the rest of invitees who

had at least one simmelian tie, the variation was very large, ranging from 2 to 208. As

mentioned, the log-transformed value of simmelian ties was used as the variable of

embeddedness (M = 2.35, SD = 1.3). As seen in Table 10, all network variables were

correlated to one another.

Table 10. Mean, Standard Deviations, and Correlations of IVs (N = 3971).

1 2 3 1 Direct Contact -

2 Personal Network Exposure

.04** -

3 Embeddedness .42** .31** -

Mean 1.32 0.12 2.35 (SD) (0.68) (0.13) (1.3) Note: **p < .01.

6.4.2. Hypothesis Testing.

One evident feature of the dataset is the violation of the assumption of

independence of observations. The network properties of each invitee are non-

independent to other invitees’ properties within the network. For example, if actor A and

B are friends with each other, their PNE cannot be independently calculated from each

other because their friendship networks are very likely to contain mutual friends.

Given that the interdependence of cases is a frequently observed phenomenon in

social network datasets, one should be careful when performing standard multivariate test

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because the correlated data can result in incorrect statistical inferences derived from the

biased standard errors (Zorn, 2001).

Two approaches may be proposed to consider the correlated data: The cluster-

specific (or subject-specific) model and the population-averaged model (Zorn, 2001).

Hierarchical Linear Model (HLM) is a representative technique for the cluster-specific

model. HLM is advantageous if a researcher hypothesizes that the covariates specific to

cluster-level should have a fixed or random effect on outcome variable. On the other

hand, Generalized Estimating Equations (GEE) is the technique to assess the population-

averaged differences in outcome variables as a function of the covariates (Hu, Goldberg,

Hedeker, Flay, & Pentz, 1998). Although HLM is advantageous to model the unit-

specific effects, HLM assumes the normal distribution of random effects at each level

(individual and cluster level). GEE results in robust estimates when the normality of

random effects are not certain or “the exact nature of the intra-cluster dependence is

unknown” (Zorn, 2001, p. 472). I used GEE considering that the clustering in my dataset

is not clear-cut for some invitees who are affiliated with more than one opinion leader’s

personal network as well as the fact that the cluster-level effect is not of interest in the

current study. GEE requires missing cases to be excluded, thus I included 3,958 invitees.

All variables were mean-centered before being put into the models.

Table 11 presents the results of the GEE predicting whether invited friends joined

the advocacy group. The result shows that those who receive multiple invitations are

more likely to join the advocacy group with the log odds B = 0.27, Wald Chi-square =

21.64, p < .001. The exponent of log odds was 1.31, indicating that the predicted odds of

joining the group changes by 1.31 times given one unit increase in direct contacts,

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holding other variables constant. Therefore, based on these results, the null hypothesis of

H1 is rejected.

In regards to H2, the null hypothesis of no difference from PNE is rejected. The

results revealed that invitees with a higher PNE toward supporters of the advocacy were

significantly more likely to show their support by joining the group: log odds B = 1.25,

Wald Chi-square = 17.48, p < .001. The exponent of log odds was even greater, 3.49 with

a large confidence interval ranging from 1.94 to 6.26. In other words, a one-unit change

in PNE increases the likelihood of being a group member 3.49 times when other variables

are held constant.

H3 hypothesized the effect of individuals’ embeddedness on their likelihood of

joining the group. Although H3 was not supported, the tests of interaction effect revealed

the contribution of the variable as a moderator. Specifically, embeddedness moderated

the influence of both direct contacts and PNE: for the interaction with direct contact, log

odds B = -0.13, Wald Chi-square = 4.55, p < .05; for the interaction with PNE, log odds B

= .89, Wald Chi-square = 11.47, p < .01. Therefore, H4b and H4c were supported. The

interaction between direct contacts and PNE was not significant, so H4a is rejected.

To examine further the way embeddedness interacts with direct contacts and PNE,

I reran the model with two different levels of embeddedness. Specifically, the high level

of embeddedness, calculated by re-setting the mean score as one standard deviation

above (SD = 1.3), was put into the model to see how the high level of embeddedness

affected the degree of influence occurring from direct contacts and PNE.

Comparing to the results from Model 1 in Table 11, which shows that the direct

contacts effect increased the likelihood of joining the group 1.31 times, the addition of

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the interaction term with the embeddedness increased the odds to 1.59 (Model 2 in Table

11). As described in Table 12, while the interaction effect on direct contacts was

significant across all three points of embeddedness, the interaction effect was the most

contributory when the embeddedness was low: When an individual’s position within the

network is not highly embedded, the direct contacts effect increases, resulting in the odds

increase to 1.89 times given the one unit change of direct contacts. In other words, less

cohesively located individuals are more greatly influenced by the direct contact from

opinion leaders.

On the other hand, the interaction effect between the embeddedness and PNE

showed a different pattern. Compared to the Model 1 (in Table 11) that revealed a 3.49

increase in the odds of outcome by one unit increase in PNE when the embeddedness is

not considered, the addition of the interaction effect increased the PNE influence to 4.86,

indicating that one unit increase in PNE increases the odds of joining the group 4.86

times if the individual’s network embeddedness is considered. As seen in Table 12, the

interaction effect is even greater for those who are highly embedded in the network: For

those who are deeply embedded in the network, the one unit change in PNE increased the

odds of joining the group 15.55 times! On the other hand, for those who are not located in

an embedded position within the network, the influence of PNE was not significant. In

other words, the influence of PNE was synergized when an individual is highly

embedded, or cohesively positioned, in a network.

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Table 11. GEE Models Predicting Contagion Effect on Invitees’ Support for the Advocacy Group (N = 3958)

Model 1 B SE Wald

Exp(B)

CI for Exp(B) Sig. Lower Upper

DC***

0.27

0.06

21.64

0.000

1.31

1.17

1.46

PNE*** 1.25 0.30 17.48 0.000 3.49 1.94 6.26

Embeddedness -0.05 0.03 1.90 0.168 0.96 0.89 1.02

Model 2 B SE Wald

Exp(B)

CI for Exp(B) Sig. Lower Upper

DC***

0.46

0.10

22.12

0.000

1.59

1.31

1.92

PNE*** 1.58 0.35 20.80 0.000 4.86 2.47 9.60

Embeddedness -0.07 0.04 3.42 0.064 0.94 0.87 1.00

DC x PNE -1.25 0.66 3.59 0.058 0.29 0.08 1.04

DC x Embeddedness* -0.13 0.06 4.55 0.033 0.88 0.78 0.99

PNE x Embeddedness** 0.89 0.26 11.47 0.001 2.44 1.46 4.10

Note: * p < .05, ** p< .01, *** p < .001; DC: Direct contact with opinion leader(s), PNE: PNE; Variables are mean-centered.

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Table 12. The Effects of Direct Contact and PNE at Three Different Levels of

Embeddedness. Predictor Embeddedness 1SD Below M 1SD Above Direct Contact B 0.63 0.46 0.3 Exp(B) 1.87*** 1.59 *** 1.34*** PNE B 0.42 1.58 2.74 Exp(B) 1.52 (n.s.) 4.86*** 15.55***

Note. n.s.: non-significant, *** p < .001, PNE: Personal Network Exposure 6.5. Conclusion and Discussions

Along with the popularity of Web 2.0-based communication technologies, e-

WOM is a commonly observed communication phenomenon in virtual social spaces.

While the majority of the literature on e-WOM targets its contribution to profit-driven

marketing and advertising sectors, e-WOM has also been an important communication

mode for social organizing activities such as social marketing and public campaigns. This

study underscores the potential of WOM communication to make Web 2.0-enabled

strategic communication particularly for the sake of the public good.

Social influence, consisting of individual and structural aspects, is understood as

the fundamental mechanism driving WOM effectiveness. Despite being as important as

individual influence, structural social influence has not been explored as rigorously as

individual influence due to the difficulty in accessing to relevant data. Taking advantage

of affordable online social network data, this chapter examined structural effects

occurring during the Facebook WOM process.

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Three mechanisms of Facebook structural influence - direct personal

recommendation, social contagion effect, and embeddedness - were conceptualized based

on the organizational social influence models: SIP and the social contagion model.

Borrowing the mathematical formalizations from social network analysis, each

component was measured and tested for how each component affected the likelihood of

the potential actor’s behavior. The findings indicate that both direct recommendation and

the contagion effect made significant contributions toward determining how the message

recipient would react.

In particular, the effect of personal network exposure was even larger than the

effect of direct recommendation. Normative influence occurs through Facebook social

networks such that people tend to follow or adopt the attitude or behavior of their own

friends. This finding suggests that the online social networks configured in Web 2.0 offer

an effective strategic communication alternative to the traditional mode of direct e-

solicitation such as sending recommendation emails or other kinds of online messages.

Such direct contacts online can be risky in that the message could possibly be perceived

as a sort of spamming that arouses a feeling of intrusiveness to a recipient. The

intrusiveness felt toward the message sender can be aggravated as the frequency that the

sender’s solicitation messages haunt the recipient’s inbox increases (Cao et al., 2009). As

advertising studies suggest, the level of intrusiveness is negatively associated with the

message effectiveness, inducing the selective avoidances against the message (e.g. Ha,

1996; Li, Edwards, & Lee, 2002; Nam, Kwon, & Lee, 2010).

On the other hand, the indirect exposure to significant others’ attitudes or

behaviors can effectively help an individual change his or her attitude or behavior toward

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the desired direction. The strategic planner can maximize contagion effects by adapting

Web 2.0 strategies that boost the accessibility to social information about others’

thoughts, attitudes, ideas, or behaviors, so-called interpersonal visibility (Friedkin, 1993).

Facebook’s social context advertising is a very exemplary strategy that attempts to pull

the most benefits from interpersonal visibility inherent in the digital networked-ness.

Because social the contagion effect occurs in a more subtle way than direct solicitation,

the emotional byproducts such as intrusiveness, boredom, or message avoidance is less

likely to be produced.

The contagion effect depends greatly on the social-structural context in which the

person is situated than the direct solicitation effect. It is evident from the findings in this

study as well in that the interaction effect between embeddedness and the contagion

effect was much greater than the interaction between embeddedness and direct contact.

While embeddedness was not an independent predictor of the outcome variable, it acted

as a moderator for other structural effects on the behavioral outcome. Particularly, a

direct personal recommendation was more effective when actors were not strongly

embedded within the network. This finding supports the argument that direct personal

recommendation is the compensatory communication mode for social influence on those

who were less integrated in interpersonal networks thus less likely to receive normative

influence from the network.

On the other hand, the embeddedness synergized the network exposure effect

such that contagion occurred 15 times more when actors were embedded in cohesive

groups than when actors were sparsely located. In other words, the role of the structural

cohesion as a moderator was more prominent when interacting with indirect exposure

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than with direct solicitation. In this sense, understanding the structural aspect of the

WOM process is a valuable task in the Web 2.0 contexts in which WOM is one of the

most common behavioral phenomena. While this project was based on a behavioral

experiment, the social-network analytic methods applied to this project lent a good start-

up to explore naturally occurring real-life WOM phenomena on Facebook or other SNS.

The project supports the potential for Facebook social networks to be an effective

venue for strategic communication. However, one should be cautious about asserting the

significance of the impact of social context advertising on promotions of the pro-social

behaviors that ask for more commitment than simple clicking. Hart and Greenwell

( 2009), for example, argues that the influence of communication technologies on the

process of fundraising might be exaggerated. They reported that less than three percent of

all fundraising is actually done online. In addition, among various online tools, the

Facebook Cause, a Facebook application developed for the purpose of nonprofits

campaign and fundraising, raises money even less than the direct e-mail solicitation (Hart

& Greenwell, 2009). Hurst (2009), the founder of the award-wining pro-bono service

foundation Taproot, criticizes that Facebook Cause “lets millions of people get on the

‘wall’ with no donation,” giving away “one of the few ‘benefits’ nonprofits can offer

donors” (n.p.).

Considering social reward as a motive is important as a public-good oriented

motive that drives social participation (Klandermans, 1984), the concern is that

Facebook-driven social organizing could impede the non-profit performance by

increasing the number of good-person-pretenders who contribute little for actual social

change. Scholars need to be aware of the tension surrounding Web 2.0, between the

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bright prospect of its potential as an effective social organizing venue and the concern

about its inane achievement. It will be the future task to explore to what extent of

commitment is expected to be stood by online actors so that social influence through

digital social networks produces a meaningful change for the social goods.

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VII. EMERGENT GROUP STRUCTURES ON FACEBOOK: SCALE-FREE,

SMALL WORLD, AND NETWORK CENTRALIZATION

7.1. Literature Background

7.1.1. E-WOM as an Online Community Building Practice

The previous chapters discussed the two factors – personal influence and

structural social influence - that determined the effectiveness of the e-WOM process.

Both are micro-level variables that influence individual’s behaviors. This chapter

explores an emergent organizational phenomenon on the macro level: How does e-WOM

communication form a social organizing process on Facebook? While WOM has been

widely viewed as a marketing effort to garner consumers’ awareness and purchasing

behaviors, the applicability of this communication mode is not limited to commercial

marketing.

The WOM process is also observed in the mobilization processes for other types

of social actions. For example, scholars have shown that social movements follow a

diffusion cycle in which social network effects determine the rate of movement progress

(Oliver & Meyer, 2003). During the mobilization process, many recruited participants are

socially influenced by members of their personal networks who are already activists (Opp

& Gern, 1993). The network effect becomes more crucial for collective action in the

digital environment in which movement organizations are transformed toward loose

social networks among members who gain increased autonomy in searching for relevant

information and pursuing tactics for action coordination (Rheingold, 2003). In this

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context of online activism, broader mobilization through individual activists’ personal

networks without institutional intervention has been noted as being even more salient

(Bennett, Breunig, & Givens, 2008). Especially along with the prevalence of social

networking services online, WOM is de facto one of the most widely adapted strategies

for social organizational goals such as social marketing, public communication

campaigns and collective political actions.

The noteworthy aspect regarding the incorporation of web services into the big

picture of strategic communication is that the audience can take an active part in the

process of communication. Web-based interactive campaigns encourage social

interactions, not only between campaigners and audiences, but also among the audiences

themselves, on which audiences can spontaneously create issue- or product-relevant

communities (Lieberman, 2001). Web 2.0 services including SNS magnify this tendency

that transforms institutionalized top-down strategic communication into a community-

building practice among likeminded end-users.

7.1.2. SNS as a Network of Personal Communities

Conceptualizing strategic communication as the process of community building

necessitates revisiting sociological discussions about how to understand community. As

Wellman, Carrington, and Hall (1988) note, community in contemporary worlds does not

merely depend on the localism or predefined socio-demographic conditions. Rather than

designating who should be members by a fixed set of criteria, community shifts from a

bounded set of membership owners who qualify the prerequisites to permeable social

networks in which the degree of social interactions with others becomes a core element to

decide a person’s affiliation with the whole.

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In other words, in contrast to modern sociologists’ concerns about the “loss of

community” (for details about the sociological skepticisms on the demise of community,

see Wellman, Carrington, & Hall, 1988, p.125) and the subsequent reduction in social

capital (e.g. Putnam, 2000), communities flourish in contemporary generations.

Communities have been transformed from being densely knit, unified and locality-based

to the sparsely knit, fragmented, and common interest-based (Wellman, 1996).

Computer-mediated communities characteristically present how the traditional

notion of community is replaced with the notion of social networks (Wellman, 1996).

Scholars have analyzed online communities in relation to offline communities. Some

implied concern that the Internet decreases community by drawing people’s attention to

mediated entertainment from face-to-face social interactions or local community

activities (e.g. Nie, Hillygus, & Erbring, 2002). Others have proposed a transformative

perspective that traditional offline communities would transmute into a new nature

affected by social, political and cultural consequences stemming from virtual community

activities (e.g. Rheingold, 2003).

Between the two extreme perspectives, it seems that scholars have dialectically

converged into a supplementation view, highlighting that online communication helps the

pursuit of relational ends by adding a means of social interaction to preexisting modes

such as telephone and face-to-face contacts. For example, the majority of online women

use email to communicate with their immediate families, relatives, and close friends

(Rainie, Fox, Horrigan, Lenhart, & Spooner, 2000). Stefanone and Jang (2007) indicate

that the majority of bloggers adopt the tool as an alternative communication mode to

share personal thoughts and feelings with others within their personal networks, although

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personality traits may moderate their usage behaviors. Scholars have noted that the

Internet helps users maintain expanded ranges of weakly tied relationship, often

uncovering “latent ties” that might have not been in touch if it were not for the digital

connectedness (Haythornthwaite, 2002; Wellman & Gulia, 1999; Wellman, Quan-Haase,

Boase, Chen, Hampton, et al., 2003). Wellman and his colleagues (2003), who have a

supplementation view, characterize the online community as “computer-supported social

networks” which sustain multiplex social ties that convey information and social support

for personal life, organizational collaboration and societal mobilization.

The supplementation perspective on online communities is especially compatible

with the rise of recent Web 2.0 social networking technologies. The tendency to build a

personal community through mediated interpersonal communication is particularly

prominent along with the recent rise of SNS. As evident in its name social networking

site, the essential goal of SNS is to serve as a web platform of building a personal

community. The goal is effectively obtained through Web 2.0 technologies by, first,

easily integrating private single-sender CMC applications (e.g. email) and open

“groupware” (e.g. discussion boards, listserv) into a unified interface (e.g. Facebook

personal profile) and, second, fomenting networking behaviors through recommendation

systems. When the SNS is successfully adopted, the website itself becomes a giant social

network that amasses individual users’ personal communities.

As Donath (2007) proposes, one innovative feature of SNS is that it enables

individuals to maintain immense egocentric networks. She calls SNS a “social supernet,”

which makes social grooming “temporally efficient” and “cognitively effective” (n.p.). A

prevalence of social supernet is an interesting online phenomenon that encourages ego-

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network scholars to redefine the preexisting understanding of personal community.

According to the anthropologist Dunbar and his colleagues (e.g. Dunbar & Spoors, 1995;

Robert, et al., 2009; Hill & Dunbar, 2003), cognitive and time constraints have been the

primary barriers that limit the size of active personal networks. Ordinary people maintain

intimate social circles of around ten individuals in which social support and emotional

attachment are primarily produced, not because of spontaneous preference to the

particular size but because of given cognitive and temporal limitations. Considering that

SNS is a useful mnemonic and time saver for relational ends, the effective utilization of

SNS may increase individuals’ capacity to manage larger social circles.

Using SNS helps users to not only enlarge the boundary of intimate personal

community but also to maintain a greater number of weak ties. This is possible by

cumulating relational histories in an online personal space and by rediscovering

relationships once forgotten through a recommendation or notification system. Moreover,

as long as relationships are interwoven (e.g., a friend of mine is also a friend of another

friend), SNS unfolds its interconnected nature across personal communities. Explication

of social interconnectedness reduces anonymous behaviors and subsequently enhances

trustworthiness toward an individual’s virtual actions.

In sum, the features of SNS, including the enlarged size of intimate relationships,

heightened reliability of virtual interactions, and the ability to reach a wide range of

social contacts, make this particular CMC platform a useful communication technology

for social organizing activities such as collective actions and information diffusion.

7.1.3. Social Network Structures in SNS

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On a micro-level, individual users construct and maintain personal communities

on SNS led by their psychological motivations, for example the desire of reputation-

building, self-presentation and impression management (e.g. Boyd, 2008; Boyd & Ellison,

2007; Lamp, Ellison, & Steinfield, 2007; Wang, et al., 2010; Walther, et al., 2009). The

majority of SNS studies discuss individual motivations based on the theoretical

framework proposed by traditional CMC research.

Although fully appreciating the insights learned from such literature, the

individualistic approach tends to limit the investigation to either a dyadic relational

situation or a controlled small group condition. Consequently, the individualistic

approach may disregard larger social contextual effects and structural outcomes

(Wellman & Guilia, 1999; Wellman et al., 2003).

Two reasons can be suggested as to why SNS scholars need to highlight structures

configured in SNS. The first is that, as far as SNS is a social space, an individual’s

behavior in SNS cannot be purely spontaneous. Once a person is aware that the self is

embedded in a larger social context, the person’s action is not solely based on his or her

free will. For example, the person might reciprocate what he owes because of the

awareness that his behavior is visible to other social actors. As much as individuals are

embedded in social networks, their behaviors in a dyadic interaction are likely to be

recognized by not only the communication partner within the dyad but also other social

relations beyond the dyads. Stated differently, social embeddedness reduces anonymous

communication and reinforces trust-building processes among community members

(Granovetter, 1985). CMC scholars need to delve into this aspect, given that anonymity

has been an important topic associated with other CMC issues such as self-disclosure,

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privacy, and disinhibition. Considering that a deceitful identity management would cost

more than the benefits if an individual is tethered to social contexts than if the individual

meets a person in isolation, elucidated interconnectedness in SNS is likely to produce a

norm of truthfulness (Donath, 2007).

The second reason SNS scholars need to look at the structural aspect is because

the collection of individuals’ micro-interactions results in the production of macro-level

group or organizational properties that can be properly understood by a structural

approach. Like other offline organizations (Monge & Contractor, 2003), SNS evolve

along with the emergent properties that are more than the mere aggregation of individual-

level activities. In a SNS, a very large network composed of multiplexed egocentric

communities, serves as a social world, which nurtures norms, conventions, roles and

responsibilities. The inquiries posited in this chapter are based on the idea that the SNS is

a form of social organization whose emergent properties can be captured through a

structural approach.

The analysis of network structural features helps reveal social mechanisms

underlying collective contingencies. Organizational studies have adopted network

analysis actively to examine the longitudinal evolution of group structures (Barnett &

Rice, 1985; Burkhardt & Brass, 1990; Shah, 2000; Barnett, 2001). The performance of a

certain community as a whole system may be predicted by evaluating network properties

such as heterogeneity, connectivity and cohesion. For example, Barnett and his colleague

have analyzed longitudinal changes in structures of international telecommunication

networks: When the individual uses of telephony were aggregated into a national level,

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the whole picture revealed a core-periphery network, supporting the validity of World

System theory (Barnett, 2001; Barnett & Choi, 1995).

While Barnett and Choi (1995; 2001) take a structural approach to understand

communication activity on a global level, other scholars have examined the structure

patterns within a bounded organizational or community settings. For example, Moody

and White (2003) found that the structure that emerged from the longitudinal co-

authoring practices among sociologists was a well-connected social network (or so-called

cohesive network), yet there was a large inequality in numbers of collaborators. This

network structure indicates that the collaborative networks among social scientists do

include superstars who have “much more influence shaping ideas than others, perhaps

acting as ‘pumps’ for ideas that are then quickly circulated through the well-connected

regions” of community, generating “generalized consensus” (Moody & White, 2003, pp.

235-236). Some scholars investigate network structures to discuss selection and social

influence processes that govern the homophily phenomena. Christakis and Fowler (2007),

for example, indicate that obese and non-obese people clustered in separate networks

more highly than the random expectation. Moody (2001) studied friendship interactions

among secondary school students. He found that the network structure exhibits the

densely-knit racially homogeneous social circles that were weakly connected to each

other, empirically supporting the existence of racial homophily among young social

actors.

Surprisingly, however, communication scholars are hesitant in applying the

structural perspective to study the evolution of online communities including SNS. It is

partly due to the lukewarm interest in using a structural approach to look at

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communication systems among the scholars, having only a few scholars in this line.

According to Newman (2001), analysis of social network structure has been an intriguing

topic for social scientists due to its important implications for the spread of information

and disease. According to him, “it is clear, for example, that variation in just the average

number of acquaintances that individuals have (also called the average degree of the

network) might substantially influence the propagation of a rumor, a fashion, a joke, or

this year's flu” (Newman, 2001, p.404).

Likewise, analyzing macro-structures of the social organizing process in SNS can

contribute to the understanding of sociological mechanisms for information and social

influence diffusion process. In an applied context, specifically, this type of analysis can

be useful to assess what kinds of social processes are underlying SNS-mediated

communication campaigns: Mapping the emergent structural patterns may help locate

positions of the influentials or the boundary-spanners. Such findings may help educate

practitioners how to recruit changing agents for more effective campaign communication

with the target community (Rogers, 2003).

7.1.4. Scale-Free, Small-World Network Structures, and Network Centralization

Network scholars have found two structural topologies widely exhibited in social

as well as natural systems: scale-free and small-world networks. These two topologies

have been observed in the formation of many social organizations, such as collaborative

networks among artists (Uzzi & Spiro, 2004), the scientific community (Newman, 2001),

co-authorship networks (Moody & White 2003) and the World Wide Web structure

(Barabasi, 2009; Park & Barnett, 2005; Barnett, Chung, & Park, in press; Hindman, 2009;

Park, Barnett, & Chung, in press). Assuming that the formation of the virtual community

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in Facebook should be a process of social organizing (e.g. mobilizing collective

behaviors), I propose that the two network properties also be observed as emergent

structures in this project.

1) Scale-free Network: A scale-free network is a network type formed based on

the ‘rule of popularity.’ According to Barabasi (2009), a scale-free network is found

universally across varied ranges of real networks from biological systems to computer

networks. A scale-free network is characterized as being highly imbalanced in the

distribution of degree centrality (i.e. number of connections a node gets from other

nodes). In other words, there are only a few extremely highly linked nodes followed by

the majority who have much fewer links. Barabasi (2009) explains that the emergence of

scale-free networks is based on two mechanisms, growth and preferential attachment:

The scale-free typology explains the process of network growth in which a new node is

added with its preference to attach to the more prestigious nodes. Stated differently, the

process of network formation follows a rich-get-richer model in which popular nodes

exponentially boost their own popularity as the network evolves (Easley & Kleinberg,

2010).

There are several motivations for preferential attachment in real social networks.

For the example of in-links structure of the Web, a scale-free network emerges because

users may be prone to hyperlink to popular websites because of the high credibility

attributed to well-acknowledged websites (Barabasi & Alberto, 1999). In cultural markets,

such as books, movies, and music, “winner-takes-all” happens not just due to the

straightforward induction from quality to success but also because consumers’ choice of

something particular over the competitors is influenced by others’ decision-making

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(Salganik, Dodds, & Watts, 2006). The expectation of the halo effect can also be a

motivation. In SNS, for example, people are more attracted to befriend a so-called

superstar who already has many followers because befriending a popular individual can

produce a halo effect through which a person might take advantage of the friend’s

reputation.

Being a real social network in which such motivations are expected to affect users’

relational behaviors, the Facebook social network is also likely to form a scale-free

topology. Furthermore, as in the offline context, communication activities on Facebook

can be conceived as “investments” to relationships “through which (a person) gains

access to embedded resources to enhance expected returns of instrumental or expressive

actions” (Lin, 1999, p. 39). In other words, Facebook is a social space in which users

build social capital through social interactions with their friends (Ellison, Steinfield, &

Lampe, 2008; Lewis, et al. 2008). In this sense, those highly connected are likely to build

more social capital, particularly entrepreneurial capital that is highly embedded in a

personal network consisting of sizable weak ties (Burt, 1992; Granovetter, 1973), than

the less connected. Entrepreneurial capital is analogous to bridging capital in that it gives

the individual the advantage of informational access and instrumental returns, rather than

emotional support inherent in bonding capital (Adler & Kwon, 2002; Lin, 2002; Putnam,

2000).

Strategic communication can be successful by taking advantage of

entrepreneurial capital of the highly connected who can exert greater social influence and

mobilize more resources than the rest of the less connected. Accordingly, when a sub-

community on Facebook is formed as a consequence of strategic communication, the

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community is likely to reveal a scale-free network structure which includes a few leading

actors who draw many new members and large portions of marginal actors.

H1: The online community formed on Facebook will show a scale-free network

structure.

2) Small-world network: Another widely observed network topology is the small-

world network. This network typology is characterized as including local clusters that are

connected by a few numbers of bridging actors. Even though members within a local

cluster may not acquaint directly with those in other clusters, members in different

clusters are able to be contacted if one passes through a few contacts. This characteristic

is widely known as “six degrees of separation,” which was first explored by Milgram

(1967) who experimented whether a letter from a randomly chosen local actor could

reach the unknown designator through social connections. Despite being imperfect, his

1967 study provided evidence for the “existence of a short path in global friendship

network,” triggering many follow-up studies across various disciplines (Easley &

Kleinberg, 2010, p. 537).

The existence of small-world networks has been empirically supported by many

real social networks, for examples scientific collaboration networks (Newman, 2001),

diffusion networks of infectious disease (Watts & Strogatz, 1998), German corporate

ownership (Kogut & Gordon, 2001), and Broadway musical collaborations (Uzzi & Sprio,

2005). Watts (1999) and Watts and Strogatz (1998) formalized the condition for small

world networks in terms of two parameters: Clustering coefficients and average path

length (the average shortest distance between pairs of vertices). Their study (1998)

identifies that a small-world network shows (a) a similar average path length to the

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average path length of random network and (b) a significantly larger clustering

coefficient than that of a random network.

The Facebook social network is assumed to present small-world tendencies

because our personal community includes multiplexed social ties that are from different

group affiliations. Accordingly, the personal network is likely to display alters being

clustered in a few groups. In addition, some alters are likely to be affiliated with multiple

groups (e.g. if a housemate is also a church member, the housemate stretches over the

cluster of housemates and the cluster of church friends, possibly playing the role of

intermediary between the clusters). However, it is expected that the extent of the small-

world tendency will be different depending on an individual’s own relational

characteristics. For example, an individual who has attended two different high schools

and transferred from one to another college will maintain a different personal network

structure – particularly in terms of number of local clusters and number of friends who

traverse different clusters – from an individual who has attended only a single high

school and college. Accordingly,

H2: The extent of exhibiting small-world tendency will be significantly different

among the examined ego-networks.

Small-world network scholars have argued that this network topology might

account for how quickly disease, rumors, and ideas can spread in a certain community or

society (Uzzi & Spiro, 2005). Carley et al. (2009) describe how the small-world network

is conceived by a graph theoretical approach:

“[Small-world network] is a type of graph in which most nodes are NOT neighbors of one another, but most nodes can be reached from every other node by a small number of hops or steps. A small world network, where nodes represent people and links connect

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people that know each other, captures the small world phenomenon of strangers being linked by a mutual acquaintance” (p.134, capitals in original).

In a small-world structure, although the majority of actors are embedded in local

clusters and are thus not aware of others’ ideas or behaviors in different clusters, the

disconnected actors are not independent because they are connected by intermediaries.

Therefore, even though locally enacting, community members are able to reach general

consensus or uniform actions on a collective level. Moreover, the pace of diffusion is

much more rapid than in a regular network (with the equal distribution of degrees) thanks

to the bridging actors who shorten path lengths between pairs of nodes (Watts & Strogatz,

1998). In this sense, as a social network reveals stronger small-world tendencies, the

network is likely to spread information and social influence more promptly and is thus

easier to be mobilized for a collective action. Based on this rationale, I hypothesize:

H3: Small-world tendency of a personal network will positively contribute to

mobilize group members.

3) Structural Difference Between the Mobilized Group and the Group of Non-

actors: I mentioned earlier that Facebook as a social organizing practice is affected by

WOM communication. The hypotheses posited above assume that scale-free network and

small-world phenomenon are the two structural characteristics that are expected to be

generically observed in Facebook social networks. One remaining question, then, is what

structural feature can uniquely be attributed to WOM-based formation of social

organization. If there is a difference between strategically evolved community and

general networks formed through ordinary networking processes, it is worth highlighting

the difference and discussing the cause. To examine the structural differences, I will

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compare three commonly investigated network characteristics: scale-free, small-world,

and additionally, network centralization.

Structural configuration of centralized network is similar to scale-free topology.

While scale free network focuses on the degree distribution of the whole network,

centralization measure highlights the discrepancy between individual actors who occupy

the most central and marginal positions. While all scale free networks should be

characterized as being centralized to some degree, there can a difference to what extent it

is centralized. Figure 10 presents two computer-generated scale-free networks that reveal

different network centralization scores. Centralization has been adopted as a useful

parameter to examine system level structures of communication networks (e.g. Barnett &

Sung, 2005; Park & Barnett, 2005; Barnett et al., in press; Park et al., in press; Lee,

Monge, Bar, & Matei, 2007).

R1: Are there structural differences – particularly, in terms of scale-free, small-

world, and network centralization – between a strategically-formed Facebook group (i.e.

formed through WOM) and the general structures configured in Facebook social

networks?

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Figure 10. Centralization Comparison between Two Scale-Free Networks

Scale Free Network 1 Scale Free Network 2

Visualization

Node 100 100 Edges 341 215 Density 0.03 0.02 Mean of Degree 4.78 4.3 SD of Degee 8.07 3.78 Degree Centralization 0.27 0.18

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7.2. Methods

7.2.1. Network Data for the Inquiry

The data used in this chapter is same as in the chapter 5: personal networks of 72

recruited egos who spread the recommendation messages to a total of 3981 alters. Among

the 3981, 883 alters successfully joined the advocacy group. Because egos who initiated

the diffusion were arbitrarily recruited by a researcher, they are essentially confederates

of the experiment. Given that it was in egos’ personal networks that the recommendation

messages were spread, it is not surprising that egos are located in the emerged

community as highly central in terms of their degrees and betweenness. Therefore, as

long as egos are put into considered, scale-free and small-world structure are highly

likely to appear because both properties assume a few highly central nodes.

Considering that egos involved in the community on the experimental purpose

rather than through naturally occurring influence process, egos and their edges were

removed from examining community structure. As a consequence, the examined

advocacy network retains only 883 alters. Figure 11 visualizes (a) a real advocacy

community with 883 nodes, the theoretically ideal (b) scale-free network and (c) small-

world network, and (d) the network with the random distribution of edges. (b), (c), and (d)

include the same number of nodes and edges to the real network.

Testing the emergence of scale-free network (H1) is based on the whole advocacy

network of 885 nodes. On the other hand, the effect of small-world tendency on the

recruitment performance (H2 and H3) is tested with each 72 ego-network as a unit of

analysis. Lastly, detecting the characteristics of advocacy community distinctive from

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general Facebook social network (R1) is based on the comparison between the advocacy

network of 885 nodes and the network composed of alters who did not join the group, in

other words, those who were not susceptible to WOM message.

Figure 11. Visualization of Network Formation: Real versus Theoretical Networks

(Nnodes = 883, Nedges = 4,479)

a. Real network of interest b. Theorized small-world network

b. Theorized scale-free network d. Randomly generated network

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7.2.2. Formalizations

Scale-free network

A scale-free network is characterized as its degree distribution following a power-

law such that, ( )~ (7)

in which the probability that a vertex in the network has connections with k other vertices

(degree k) in a power law with the exponent r (Barabasi & Alberto, 1999). The power-

law distribution is represented linearly when plotted on a log-log scale (Moody, 2003).

Put mathematically, ( ) = → ( ) = ln( ) − ( ) (8)

To examine scale-free network, degree distribution of the advocacy group was

computed. Then, the degree and the frequency of each degree were log-transformed to

test whether a linear relationship is established between the log-transformed values.

Small-world network

A small-world network is characterized by two network properties: Average path

length (L) and average clustering coefficient (C). Average path length refers to the

average of the shortest distance, so-called geodesic (Wasserman & Faust, 2004), between

every pair of vertices. The average clustering coefficient is the average of the density of

sub-graphs, each of which is composed of a set of neighboring vertices that are directly

connected to each vertex i, and the subsequent edges. For a detailed formalization of the

clustering coefficient, refer to the formula (2a) in Chapter 4.

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Watts and Strogatz (1998) formalize the properties of a small-world network

(with N vertices and K edges per vertex) such that: ≈ ~ (9a)

and

≫ ~ (≪ 1) (9b)

Accordingly, if a network is considered as small-world, L/Lrandom (L- ratio) is

close to 1 (but not less than 1) and C/Crandom (C-ratio) is much greater than 1. Based on

these formalizations, I computed L and C of each ego-network and the expected

approximates of Lrandom and Crandom of the counterpart random network of each ego-

network.4 Exploration of the small-world network also needs to meet two preconditions:

First, every node needs to be reachable (Watts & Strogatz, 1998; Moody, 2003) and

second, the number of edges per vertex K has to be bigger than the network size N (Watts

& Strogatz, 1998). Accordingly, the examination of the small-world phenomenon was

based on the biggest “component” (the network in which nodes are all connected;

Wasserman & Faust, 1994) of each ego-network. The networks that include K less than N

were left out for computation.

Network Centralization

Network centralization quantifies “the range or variability of the individual

actor’s (centrality) indices” (Wasserman & Faust, 1994, p.176). If the gap between the

4 To make sure that the computed Lrandom and Crandom are the correct representation

of random network properties, I generated 100 random networks including the same number of vertices and edges to the real network and compared the mean score of L and C of 100 random networks with Lrandom and Crandom, which were computed following Watts’s and Strogatz’s procedure (1998). The results were close, justifying the use of Watts’s and Strogatz’s (1998) computation.

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most highly central individual(s) and the least central individual(s) is big, the resulting

centralization score is also large.

Degree centralization measures how imbalanced the vertices are in terms of the

number of edges they have. It is formalized by Freeman (1978/1979):

= [∑ ( ∗) ( )][∑ ( ∗) ( )] (10a)

where the numerator ( ∗) is the largest observed value of degree and the ( ) is the

degree of the vertex i and the denominator is the maximum possible difference between ( ∗) and ( ). The denominator is directly calculated as equal to (n-1)(n-2). Thus,

= ∑ ( ∗) ( )[( )( )] (10b)

Betweenness centrality is defined as the heterogeneity of the betweenness of the

members of the network. Here, betweenness refers to the extent to which a vertex is

located on the shortest path between two other vertices (Wasserman & Faust, 1994). The

more a vertex is passed through, the higher the vertex’s betweenness centrality is. In a

social network perspective, betweenness centrality represents an actor’s ability to control

social interactions or information transfer between pairs of other actors in the network.

Therefore, betweenness centralization refers to the imbalance of individual actors’ ability

to control the information or behavioral flow. Freeman (1978/79) formalized

betweenness centralization,

= ∑ ( ∗) ( ) (11)

7.3. Results

Scale Free Network?

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The mean degree of the 883 group members was 5.07 (SD = 5.61) and non-group

members (M = 16.05, SD = 19.07). Although non-group showed higher mean degree

when un-scaled, the comparison between normalized versions, which divides simple

degree by the maximum degree possible (Freeman, 1971), results higher mean degree for

group members than no-group member (for group members, M = .00573, SD = .00635;

for non-group members, M = .0052, SD = .00619). There was a statistical difference

between normalized mean degrees, t(3,970) = - 2.23, p = .026.

Figure 12 shows the degree distribution resulting from the advocacy group.

Evidently, the distribution is highly skewed, revealing the possibility of a power-law

relationship. Statistical testing showed that the observed distribution fit a power law,

showing a significant linear relationship between the log of degree and the log of

frequency in which each degree appeared: F(1, 32) = 228.60, r2 = .88, adjusted r2 = .87, p

< .001 (Figure 13). Therefore, the null of hypothesis 1 is rejected.

Meanwhile, the relationship is not strictly linear in that 13 percent variances

unaccounted for by the power-law. The regression analysis with an additional block

including a squared term to the regression explains 6.6 percent more of the variance,

Fchange(1,31) = 35.54, p < .001, improving the model fit to r2 = .94, adjusted r2 = .94. In

other words, whereas a scale-free typology plays a large part in structuring the emerged

network, additional mechanisms also contribute.

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Figure 12. Degree Distribution of Members in the Advocacy Group (M = 5.07, SD

=5.61).

Figure 13. Log-log Plot to Test Scale-Free Network.

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Small-World Effect on Mobilization

As mentioned above, one of the preconditions that needs to be qualified prior to

testing the small-world phenomenon is connectivity among the nodes: All nodes have to

be reachable by one another. To meet the precondition, the subnetworks composed of the

biggest component were generated from the original data of each ego-network (I term the

component-based sub-network of each ego network componet hereupon).

Another precondition for the investigation is that K should be larger than N. A

total of 68 components met the second precondition, and were thus analyzed further.

While the mean size of the original ego networks was 79.10 (SD = 53.82), the network

size reduced to 73.00 (SD = 52.45) for the components. The average number of edges a

vertex has is 14.15 (SD = 12.70). The average path length of each componet was 2.12

(SD = .52) with an average clutersting coefficient of 0.55 (SD = .15). The mean of small-

world parameter L-ratio was 1.20 (SD = .25) and C-ratio was 3.03 (SD = 1.69). Table 13

summarizes the descriptive statistics.

Table 13. Descriptive Statistics of 67 Componets.

Min Max M SD Component Size (N) 4.00 222.00 73.00 52.45 Size of Original Network 5.00 222.00 79.10 53.82 Edges Count 8.00 9984.00 1354.15 1868.76 Edges Count/N (K) 1.53 72.88 14.15 12.70 Average Path length (L) 1.19 3.39 2.12 0.52 Clustering Coefficient (CC) 0.19 0.88 0.55 0.15 L/Lrandom (L-ratio) 0.20 1.58 1.20 0.25 CC/CCrandom (C-ratio) 1.03 9.99 3.03 1.69 Note. Only the biggest components considered for each ego-network

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Hypothesis 2 posits that ego-networks will have a differentiated tendency toward

small-world structure due to the dissimilar personal background in which an ego has

formed and maintained social relationships. Two parameters of small-world structure, L-

ratio and C-ratio revealed significant differences among the components: For L-ratio, t(66)

= 39.838, p < .001; for C-ratio, t(66) = 14.716, p< .001, supporting the hypothesis.

Hypothesis 3 posited the positive effect of the small-world structure on the actual

outcome of mobilization. To test the hypothesis, I split the cases into two groups: one

showing the small-world tendency and the other not showing it. The division was based

on the theoretical parameters, i.e. the L-ratio should be close to 1 (but not less than 1) and

C-ratio should be much greater than 1. Here, I used the mean scores as specific criteria

such that a network is considered as small-world, if (a) the L -ratio is equal to or less than

1.20 with the lowest bound 1 and (2)the C-ratio is equal to or larger than 3.03.

Following this criteria, 27 networks were identified as having a small-world structure,

while 40 were not considered so.

Regression analysis was conducted with the number of recruited alters as a

dependent variable.5 The dependent variable was coded into a 7-point scale due to the

skewness. As previously explained in chapter 4: On average, 3.89 (SD = 1.42) alters were

mobilized to join the group from the ego networks that had a small-world structure, and

5 One might argue that a sample size of 67 is too small to use as a model for the sequential regression analysis. Given that the unit of analysis is not an individual but a whole network, however, difficulty in data collection has been widely understood and a relatively small sample size has also been excused. Many preexisting group/organizational studies that explore network effects on group-level performance conducted regression analyses with even smaller numbers of cases (and with more independent variables). For examples, see Sparrowe, Linden, Wayne, and Kraimer (N = 38 cases; 2001) , Mehra, Dixon, Brass, and Robertson (N =28; 2006) , and Rulke and Galaskiewicz (N = 39; 2000) .

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2.28 alters (SD = 1.22). The regression analysis result revealed that, with the network size

controlled, the small-world tendency has a positive effect on the alters’ joining the group,

accounting for 2.2% of the additional variances: F (2, 64) = 61.82, p < .001, R2 = .66,

Adjusted R2 = .65. In other words, recruitment by an ego was more effective when an

ego-network has a small-world structure (Table 14).

Table 14. Small-world Effect on Network Recruitment (N = 67).

Model Variables B SE Beta t 1 (Constant) 1.137 .201 5.665

Network Size*** .023 .002 .800 10.753

F(1,65) = 115.63, p < .001 , R2 = .64, Adjusted R2 = .64 2 (Constant) 1.113 .196 5.674

Nework Size*** .020 .002 .715 8.571 Small-World?* .529 .257 .172 2.060

Fchange (1,64) = 4.24, p < .05, R2change = .022

Final F (2, 64) = 61.82, p < .001, R2 = .66, Adjusted R2 = .65 Note. *** p < .001, * p < .05

Structural Comparison between the WOM-based Community and the General Social

Networks on Facebook

Finally, structural characteristics are compared to see whether there is any

difference between the strategically formed social networks and the generically

configured networks on Facebook. To do so, I compare the two social networks: the

advocacy group that emerged from this project (N=883) and the network including the

rest of the alters who did not respond to the recommendation (N= 3,087). Three types of

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structural characteristics are examined: scale-free, small-world, and network

centralization (degree and betweenness).

While it is possible to compare the scale-free structure between the networks of

dissimilar sizes, comparing small-world structure and centralization cannot be directly

performed between the different sizes due to the measurement sensitivity of network size.

Specifically, as seen in the formulas above, the calculation of clustering coefficients and

average path lengths are not independent from the number of edges that exponentially

increases as the network size grows. Also, the maximum difference between two vertices’

centrality, which is put as a denominator when computing centralization, is also

influenced by the network size – the larger the network size, the bigger the maximum

difference is. Therefore, small-world structure and centralization are tested not by direct

comparison between the two networks but by the following procedures: (1) generating

random expectations of each network, (2) producing the ratios between the real and

random values for each network then, (3) eyeballing how different the ratios are.

1) Scale-free networks: Hypothesis 1 above tested the existence of scale-free

properties in the emergence of the advocacy group. Scaling in a log-log plot found a

negative linear relationship between degrees and their frequencies, supporting that the

degree distribution followed a power-law. The equivalent procedure was performed

based on the degree distribution of the social network composed of unresponsive alters.

The result was strikingly similar to the advocacy network. Furthermore, when the non-

linear term was added to the model of advocacy social network, 6.6 percent variance was

additionally accounted for. This result was also the same for the case of non-actors

network: The same amount of variance, 6.6 percent, was accounted for by adding the

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squared term. The equivalent results between the advocacy group and non-actor group

suggest that the scale-free network is not a unique structure that emerged as a

consequence of strategic communication. Rather, it is a universal social network structure

on Facebook. Table 15 shows how similar the results are between the two networks

when it comes to the scale-free property.

Table 15. Scale-Free Structure of Strategically Emerged and Generic Social Networks on

Facebook.

a. Advocacy network formed through WOM communication

R R2 Adj. R2 SE

Change Statistics

R2change

Fchange(df1,df2)

Linear .937a .877 .873 .51610 .877 228.603(1,32)***

Linear +Non-linear .971b .943 .939 .35791 .066 35.536(1, 31)***

b. Network composed of alters who were not affected by WOM Communication

R R2 Adj. R2 SE

Change Statistics

R2change Fchange(df1,df2)

Linear .936a .877 .876 .47260 .877 733.154(1,103)***

Linear +Non-linear .971b .943 .942 .32237 .066 119.376(1,102)

*** Note. *** p < .001

2) Small-World Networks: Components of the advocacy network (N = 665) and

the network of unresponsive alters (N = 2,087) were created. For a better understanding,

Table 16 compares the results from this study to the four preexisting well-known small-

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world networks (Kogut & Walker, 2001; Watts & Strogatz, 1998): The film actors

network is a co-participation network of Hollywood actors in films. The power grid

network is the network among generators, transformers, and substations in the western

United States. C. Elegans is the neural network of a worm. Last, the German network is

a network of German firms connected through ownerships.

Table 16. Small-World Network in Facebook: A Comparison

Path Length Clustering Coefficient

Actual-to-Random Ratio

Data Source Network Actual Random Actual Random L-ratio C-ratio

W&S Film-actors 3.65 2.99 0.79 0.001 1.22 2,925.9 Power grid 18.7 12.4 0.08 0.005 1.51 16 C.Elegans 2.65 2.25 0.28 0.05 1.18 5.6

K&W German 5.64 3.01 0.84 0.022 1.87 38.18

Facebook Advocacy 5.57 3.68 0.48 0.01 1.51 46.48 Unresponsive 4.65 3.05 0.5 0.006 1.52 80.93

Note. The references for comparison: W&S - Watts and Strogatz (1998), K&W - Kogut & Walker (2001); PL - Path length, CC-Clustering Coefficient

As seen in Table 12, both networks reveal similar path lengths to and greater

clustering coefficients than each respective random network, supporting the small-world

structure. In other words, the small-world network, like the scale-free network, is also a

universal structure of Facebook social networks rather than to be induced from the

instrumental communication.

3) Network Centralization: First, degree and betweenness centralization were

measured for the advocacy network and the unresponsive alters’ network. To compare

the centralization of the real Facebook networks to their respective random expectations,

100 random networks were generated with an equal size and density to each network.

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Then, the degree and betweenness centralization values were calculated for each random

network (Appendix I). Random expectations are derived by averaging the values.

Table 17 shows the values of degree and betweenness centralization. The actual

centralization values were not very discrepant between the advocacy network and the

non-actors’ network: The values of degree centralization for the two real groups were

0.0419 and 0.0473 respectively, and the betweenness centralizations were 0.0957 and

0.0579. One the other hand, the values derived from random networks showed larger

discrepancies: The random expectation of degree centralization for the advocacy group

was 0.0096 and between centralization was 0.0117, while the random expectation of

degree centralization and betweenness centralization were notably smaller for the non-

group network, 0.0052 and 0.0012 respectively.

Consequently, the ratios of centralization of the advocacy network to its

respective random expectations turned out to be smaller than the ratios of the

unresponsive alters’ network to its random expectations. Specifically, the degree

centralization ratio for the advocacy network was 4.3649 compared to 9.0961 of the

unresponsive network. The betweenness centralization ratio was even more remarkably

different: For the advocacy network, it was 8.1795, while the ratio was 48.25 for the

unresponsive network.

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Table 17. Degree and Betweenness Centralization: A Comparison

Degree Betweenness Actual-to-

Random Ratio Network Actual Random Actual Random D-ratio B-ratio Advocacy Network 0.0419 0.0096 0.0957 0.0117 4.3649 8.1795 (Nodes =883, Edges = 4479) Unresponsive Network 0.0473 0.0052 0.0579 0.0012 9.0961 48.25 (Nodes = 3087, Edges = 49561)

Note. D-ratio: Degree centralization ratio; B-ratio: Betweenness centralization ratio.

7.4. Conclusion and Discussion

This chapter explored structural characteristics of the Facebook social network,

particularly with a focus on the emerged community structure led by WOM-based

strategic communication. Based on the widely known network properties – scale-free,

small-world, and network centralization – the structure of the advocacy group mobilized

through the cyber-behavioral experiment was examined. The experiment was to motivate

people to join the advocacy group by spreading recommendation messages through

confederates’ ego-networks.

The first interesting finding is that the structure of the ego-network contributed to

the communication performance. While it is not surprising that the size of the ego-

network would affect the number of alters mobilized from the ego-network, it is a novel

finding that the structural characteristic additionally contributed to the confederates’

mobilization performance. Specifically, confederates could draw more alters into the

advocacy group when their ego-networks were characterized as having a small-world

structure.

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Another finding is that both scale-free and small-world structures were manifested

in the community that emerged. A scale-free model was the most obvious in that this

characteristic explained about 87 percent of the variance of degree distribution. The

small-world network also appeared to be valid, probably playing a role in the remaining

unaccounted variance. These findings suggest that, first, the network was formed by a

handful of leading actors who not only have many social connections but also are able to

exert social influence enough to change others’ attitudes or behaviors. Second, the

participation motivations are likely to be spread through the coherent friendship networks,

which are not segregated yet are connected through a few of actors bridging multiple

networks. By displaying scale-free and small-world structure, the advocacy network

shows the potential to be an effective communication system characterized as having

leadership (i.e. highly central actors), member coherence (i.e. strong local clustering), and

rapidly distributing general consensus (i.e. by connecting local clusters through small

numbers of message transmitters).

Both characteristics, however, turned out not to be the unique properties

contingent on the strategic efforts for social organizing. In contrast, the comparison of the

advocacy network to the network composed of the unresponsive alters revealed that the

scale-free and small-world are universally observed structures throughout general social

networking processes on Facebook. That is, the analysis of the scale-free network

resulted in a surprisingly similar pattern between the two. Regarding the small-world

structure as well, the examination of the actual-to-random ratios revealed that both the

advocacy and the unresponsive group have small-world tendencies, although the resulting

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ratio for the clustering coefficient was smaller in the advocacy group than in the

unresponsive group.

The fact that Facebook social networks are generally characterized as both small-

world and scale-free is interpreted as both good and bad news for communication

strategists. The good news is that the structural advantages of Facebook can be easily

adapted regardless of one’s expertise in strategic planning. The Facebook social network

can be a convenient communication channel to reach general audiences rapidly as well as

widely. On the other hand, it can be bad news that structural difference is hardly found

between the networks composed of conscious actors and of general audiences. This lack

of difference can raise doubts that special investment in Facebook social-organizing

would necessarily return better performance due to the limitation of forming structurally

better networks (for example, by remarkably reducing the communication pathways, or

by decentralizing the network while keeping cohesiveness).

Meanwhile, the strategic group and the unresponsive group showed differences in

centralization. When compared to random expectations, both degree and betweenness

centralization were revealed to be greater for the unresponsive than the strategically

emerged group. Particularly, the betweenness centralization of the unresponsive group

was far more intense than the advocacy network.

One explanation for the difference is the number of isolates. Considering that

isolates’ degree and betweenness centrality are zero, a larger number of isolates is

directly associated with a bigger gap between the maximum and the lowest centrality. If

this is the case, one can conclude that the purposively formed network shows higher

connectivity by including fewer numbers of isolates and showing lower network

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centralization than general social networks on Facebook. High connectivity suggests that

the advocacy network in this study must have been constructed by the spread of social

influence among connected people. In other words, the WOM communication was at

work in the process of constructing the advocacy network. The high connectivity also

implies that the advocacy group has the potential to achieve a higher level of consensus

and better unification than general networks composed of indifferent people.

Another explanation, however, is that the difference of centralization could be

simply due to network size: Isolates are likely to be produced more as network size grows.

Even if each network was compared to its own random expectation, the higher likelihood

to include isolates in the unresponsive group, which includes three times more nodes than

the advocacy group, could affect the results of greater centralization. If this is the case, it

is possible to find no remarkable structural differences between the strategic social

organizing and the ordinary social networking in Facebook and lead to the discussion of

the ‘double-edged sword’ above.

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VII. CONCLUSION AND DISCUSSIONS

The current dissertation attempted to model social influence occurring in the Web

2.0 environment. Particularly, one of the most popular Web 2.0 services, Facebook, was

examined. While the majority of the preexisting CMC literature looks at the dynamics of

interpersonal and group communication by focusing on individual psychological factors,

this dissertation points out that an individual’s online behaviors is not just governed by

intra-individual processing but also by social contextual factors. The influence of social

environments on shaping a person’s thoughts, attitudes, or behaviors is conceptualized as

social influence.

Social and interpersonal influence has been a popular topic among

communication scholars. This dissertation adapted the influence literature developed in

offline-based organizational and strategic communication to delve into multifarious

influence mechanisms affecting online users’ attitudes and behaviors. Theorizing social

influence in the Web 2.0 context is a worthwhile task for applied communication studies

in that social influence is the fundamental process underlying e-WOM communication,

one of the most widely adopted interactive marketing and campaign strategies. Especially

along with the rise of social media, the WOM strategy is convenient not just for

commercial marketers but also for community organizers of social marketing, political

campaigns, activism, or non-profit fundraising.

Two important aspects of social influence have been studied: personal influence

and structural social influence. Personal influence was conceptualized as the influence

exerted by opinion leadership. Chapter 5 explored how individuals’ Facebook social

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characteristics are associated with their opinion leadership. Structural social influence is

understood as social network effects. In chapter 6, I measured network structural

properties of each message recipient within the Facebook friendship community and

tested how the network properties contributed to a recipient’s subsequent behavior.

To theorize and empirically test social influence mechanisms, I conducted a

cyber-field behavioral experiment by having 128 confederates spread advocacy messages

to their Facebook friends. The expected behavior from the message recipients was to

support the advocated issue. The support could be expressed by joining the relevant

Facebook group. Accordingly, the collective consequence of recipients’ positive

responses to the message is the emergence of social organization that was strategically

formed. The project collected the data through a mixed method combining a conventional

survey, computer-generated personal network data and behavioral observation.

The summary of the findings are as follows: First, Chapter 4 examined the

characteristics of the Facebook influentials. The study found that the Facebook

influentials, at least in this project, were characterized as “social connecters” rather than

“experts.” Facebook influentials showed more active online community participation,

larger personal network size, and more heterogeneous personal network structure than

less influential others. The Facebook influentials identified in this project exerted

normative influence rather than informational influence, as seen by the lack of significant

findings associated with the knowledge-based opinion leadership. The study contributed

methodologically to the development of Web 2.0 opinion leadership literature by

comparing two different opinion leadership measures and by applying a social network

analytic technique to measure social attributes, particularly cosmopoliteness.

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The findings in this project, however, are preliminary due to the possibility of bias

induced from the experimental design. To elaborate, the advocated object in this project

was to mobilize collective behaviors for common good rather than to make an adoption

decision for an individual’s own sake. The intended behavioral response was also an

easy and straightforward kind, which was simply joining the group. Behavioral

compliance can be a qualitatively different issue from attitudinal change. Given that

opinion leadership is more about influencing thoughts and attitudes, rather than

behavioral change, a stronger presence of normative influence than informational

influence could be due to the issue contextualized in this project. To ensure the external

validity of the results, future research needs to be conducted in other topical contexts.

Chapter 5 examined the structural aspect of Facebook social influence.

Considering SNS-specific context, I devised the typology of structural influence

mechanisms. Classical social influence models developed in organizational studies,

including SIP and the social contagion model, were adapted to categorize sub-

mechanisms. Three different sub-mechanisms were identified: direct recommendation,

social contagion, and network embeddedness. The results found that direct contact by

multiple message senders increased the likelihood of a recipient’s compliance to the

message by becoming a member of the group. This direct recommendation effect turned

out to be greater among those who are less integrated with others in personal

communities. On the other hand, the social contagion effect was revealed to be even

larger than the direct contact effect. Stated differently, a message recipient was more

likely to join the group if the recipient perceived that his or her friends are also group

members. The visibility of indirectly acquired social information is a distinctive

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characteristic of SNS in comparison to traditional forms of CMC. The contagion effect

was revealed to be particularly large when a recipient was deeply embedded within the

community. The findings showed that direct contact complemented the social influence

process for those who were superficially embedded, while the contagion effect was

synergized among those who were deeply embedded within Facebook networks.

SNS social networks and structural social influence theory can supplement each

other. Considering that the paucity of literature on structural social influence is partly due

to the rarity of complete structural data, online social network data is relatively easily

accessible from SNS and thus can be effectively utilized by the influence scholars. At the

same time, SNS scholarship examining social influence needs to pay attention to the

structural context where social influence occurs because interpersonal visibility through

social networks is more salient than any other communication modes. Theories of

structural social influence lay the groundwork to explain the influence phenomena

occurring in SNS.

Chapter 7 is not directly related to social influence process occurring on an

individual or interpersonal level. Instead, it implies that the micro-level of social

influence process in SNS can result in organizational behaviors as a collective

consequence. The macro-structures were examined as to whether any systematic

structural pattern is observed in the emergent communication network through Facebook

direct recommendations and social contagion. The test of two well-known network

properties –scale-free, and small-world – revealed that, while both structures

characterized the emergent communication network, they were not uniquely contingent

on the advocacy community that was formed strategically but universally presented in

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general social networks in Facebook. Centralization analysis, on the other hand, showed

that the community built through the WOM included fewer disconnected nodes (or

isolates) than the generic social network, implying that a strategically emerged

community has the potential to form a cohesive network through which general

consensus is more easily attained than a naturally-occurring community without any

involvement of changing agents. The results are tentative, though, requesting future

studies in different contexts.

Monge (1987) emphasizes that human communication processes are closely

related with the structure of social relationships. Although seeming to be a stable

environment in which communication activities are exchanged, structure is changing

constantly as well. In other words, communication structure evolves (Monge &

Contractor, 2003). Examining the emergent network structure helps assess the

communication process within the community or organization, such as the speed, breadth

and diversity of information flow, quality of group performance and the emergence of

leadership.

Given that the evolution of communication networks from formation to

disbandment (or termination of activities although the space still exists) is prevalently

observed in an expedited way in Web 2.0., structural analysis can benefit communication

practitioners who try to incorporate the online community as a part of communication

strategies. It is a preliminary stage, however, for communication scholars to visualize and

characterize the system-level of network properties. An important question still remains

unanswered: How would each structural property be strategically advantageous in

different kinds of communication context? More empirical investigations need to be

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made in various situations to build up the theory that foresees the impacts of macro-

structural characteristics of Web 2.0-based community on communication performances.

The project is not free from limitations. By pointing out limitations, I propose

some future research directions. As mentioned above, the first issue is the simplicity of

the intended behavioral outcome. The influence mechanisms become more complex and

dynamic when the issue at hand is more complex and urgent as a personal matter. More

complex real life cases are abundant. For example, a researcher can examine the spread

of health care information. The subsequent questions can be: How does health care

information (e.g. drug information) spread over Facebook social networks? Does the

spread of information affect a user’s health-related attitude or behavior (e.g. preference

for or adoption of a particular drug product)? If so, whose information provision is more

influential? Which network position is more susceptible to the informational or normative

influence? How does the word-of-mouth process affect the message recipient’s judgment

whether the information is correct or misleading? Besides health care, prevalent social

organizing practices for emergency response, election campaigns, collective political

action, and charity fundraising might also be examined based on the theories and methods

utilized in this dissertation.

The second important limitation is that the project disregarded the longitudinal

aspect when analyzing social contagion. Personal network exposure (PNE), the parameter

for the contagion effect, is defined as the proportion of alters who have already adopted

before an individual made a decision (Valente, 1995). Accordingly, PNE is supposed to

be measured in consideration of time period. Unfortunately, the current dataset does not

include information about the time of enactment for each individual. Alternatively, PNE

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is measured by simply counting the proportion of adopters within a personal network by

the time the data collection was finished. This approach can lead to a misunderstanding

of the contagion process because it is possible that the direction of influence give-and-

take is reversed. Instead, a person of interest was influenced by the exposure to his or her

friend’s behavior, and the person’s behavior could have exerted influence on his or her

friend’s behavior. Consideration of the temporal aspect will increase the validity of the

existence of social contagion effects.

Another limitation can be pointed out in that the study is the consideration of the

dichotomized relational aspect, whether two are linked to each other as a friend in

Facebook or not. As mentioned earlier, our personal community consists of multifarious

relational types, ranging from intimate relationships to those latently tied, to those who

are not even activated yet as relationships. Network scholars term the quality of

relationship as tie strength. As one of the indicators of network cohesion (Burt, 1987;

Meyer, 1994), tie strengths are likely to convey different levels of interpersonal influence.

While this study could not capture the quality aspect of Facebook social interactions,

Easley and Kleinberg (2010) say that one benefit of using online social networks as data

for analysis is the availability of objective information about the amount of social

interactions. Specifically, log-files can be used as a good start to explore the history of

interactions, the level of intimacy, and communication frequencies that an ego has with

alters identified in the egocentric network. Using log-files is also advantageous in that it

not only lessens ego’s burden to report the interactional natures about each of ‘ego-alter’

and ‘alter-alter’ relationships but also minimize the perceptual error that might arise due

to the ego’s misperception or incorrect memory.

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Lastly, future studies can take a closer look at the evolutionary process of the

Faccebook group. A preliminary analysis found that the mobilization process follows the

recently highlighted diffusion pattern of the r-shape curve (Barnett, et al., in press;

Danowski, et al., in press). This curve indicates that the critical mass is reached in an

increasingly rapid speed, implying that a great amount of related messages are produced

simultaneously and the adoption behavior occurs with the minimal level of cognitive

learning process. Such a pattern is characteristically observed in many ICT-based

diffusion processes. It will be an interesting topic of inquiry what kind of factors drive

such mobilization or diffusion process. Possible factors are the network exposure level

within interpersonal communication networks, the crowd behavior motivated by

perceiving the increased group popularity, or the organizational credibility checked by

observing quality group activities and social interactions. Also, by longitudinally tracking

relational chains of who was influenced by whom, future studies can integrate the

evolutionary perspective into the exploration of the social influence process on Facebook.

Communication technologies have been aggressively adopted for strategic

communicators. Both commercial and non-commercial sectors can take advantage of

technology-mediated communication to achieve instrumental goals. The online social

network flourishing in Web 2.0 services is being said to have potential to facilitate the

WOM process. It is timely to theorize Web 2.0 interpersonal and social influence

mechanisms to understand the effectiveness of WOM-based communication. While the

current project adopted a structural approach to personal influence and social network

influence, inquiries on the effect of individual attributes, particularly the message

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recipients’ and the message valence might also be integrated into the full story of the

Web 2.0 influence.

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APPENDIX I. Simulation Results of Centralization for Random Networks

Network Centralization for Generated Random Networks (Nnodes =885, Nedges = 4479

Network Centralization for Generated Random Networks (Nnodes = 3087, Nedges = 49561)

Between Closeness Degeree Between Closeness Degeree 1 0.0113 0.0302 0.0079 1 0.0012 0.0561 0.0045 2 0.0106 0.0275 0.0090 2 0.0013 0.0636 0.0048 3 0.0099 0.0246 0.0101 3 0.0011 0.0602 0.0052 4 0.0101 0.0286 0.0079 4 0.0012 0.0659 0.0045 5 0.0135 0.0287 0.0090 5 0.0014 0.0622 0.0065 6 0.0152 0.0295 0.0079 6 0.0012 0.0628 0.0048 7 0.0094 0.0358 0.0079 7 0.0014 0.0585 0.0052 8 0.0107 0.0372 0.0113 8 0.0012 0.0536 0.0052 9 0.0114 0.0190 0.0090 9 0.0012 0.0724 0.0048

10 0.0119 0.0367 0.0090 10 0.0014 0.0695 0.0045 11 0.0150 0.0580 0.0090 11 0.0014 0.0612 0.0058 12 0.0100 0.0230 0.0101 12 0.0014 0.0582 0.0052 13 0.0111 0.0461 0.0101 13 0.0013 0.0582 0.0045 14 0.0165 0.0535 0.0101 14 0.0013 0.0652 0.0061 15 0.0126 0.1123 0.0090 15 0.0014 0.0674 0.0048 16 0.0095 0.0225 0.0079 16 0.0011 0.0623 0.0052 17 0.0110 0.0238 0.0101 17 0.0014 0.0581 0.0048 18 0.0133 0.0188 0.0090 18 0.0013 0.0662 0.0052 19 0.0107 0.0448 0.0090 19 0.001 0.0601 0.0052 20 0.0130 0.0495 0.0090 20 0.0012 0.0595 0.0048 21 0.0129 0.0742 0.0101 21 0.0013 0.056 0.0052 22 0.0118 0.0296 0.0079 22 0.0011 0.0593 0.0052 23 0.0115 0.0275 0.0135 23 0.0012 0.0586 0.0052 24 0.0107 0.0274 0.0101 24 0.0014 0.062 0.0058 25 0.0105 0.0340 0.0090 25 0.0012 0.0639 0.0048 26 0.0102 0.0254 0.0113 26 0.0013 0.0601 0.0052 27 0.0105 0.0264 0.0090 27 0.0013 0.0655 0.0055 28 0.0094 0.0355 0.0090 28 0.0016 0.0681 0.0048 29 0.0093 0.0442 0.0124 29 0.0012 0.0665 0.0052 30 0.0119 0.0748 0.0101 30 0.0013 0.0601 0.0061 31 0.0164 0.0311 0.0113 31 0.0011 0.0618 0.0055 32 0.0122 0.0251 0.0090 32 0.0009 0.054 0.0048 33 0.0128 0.0226 0.0101 33 0.0013 0.0564 0.0045

178

34 0.0109 0.0408 0.0124 34 0.0013 0.0583 0.0055 35 0.0098 0.0286 0.0113 35 0.0012 0.0631 0.0055 36 0.0115 0.0274 0.0090 36 0.001 0.0564 0.0048 37 0.0092 0.0362 0.0090 37 0.0012 0.0567 0.0048 38 0.0156 0.0178 0.0135 38 0.0012 0.0624 0.0052 39 0.0107 0.0264 0.0101 39 0.0011 0.0636 0.0048 40 0.0132 0.0726 0.0113 40 0.0011 0.0575 0.0048 41 0.0132 0.0451 0.0101 41 0.0013 0.0616 0.0048 42 0.0095 0.0218 0.0113 42 0.0014 0.0602 0.0055 43 0.0125 0.0240 0.0135 43 0.001 0.0673 0.0048 44 0.0110 0.0235 0.0090 44 0.001 0.0562 0.0048 45 0.0117 0.0406 0.0090 45 0.0012 0.0599 0.0052 46 0.0098 0.0479 0.0079 46 0.0012 0.0571 0.0048 47 0.0208 0.0251 0.0135 47 0.001 0.0559 0.0052 48 0.0097 0.0440 0.0090 48 0.0013 0.0554 0.0052 49 0.0124 0.0428 0.0090 49 0.0012 0.0646 0.0052 50 0.0099 0.0496 0.0079 50 0.0014 0.0623 0.0048 51 0.0118 0.0329 0.0101 51 0.0012 0.0631 0.0052 52 0.0088 0.0488 0.0113 52 0.001 0.0563 0.0042 53 0.0165 0.0183 0.0090 53 0.001 0.0622 0.0058 54 0.0141 0.0237 0.0090 54 0.0011 0.058 0.0058 55 0.0100 0.0232 0.0079 55 0.0013 0.061 0.0052 56 0.0110 0.0343 0.0124 56 0.0012 0.0595 0.0058 57 0.0121 0.0295 0.0090 57 0.0013 0.0594 0.0052 58 0.0122 0.0341 0.0113 58 0.0012 0.0616 0.0055 59 0.0096 0.0371 0.0101 59 0.0015 0.072 0.0058 60 0.0124 0.0442 0.0090 60 0.0012 0.0578 0.0052 61 0.0118 0.0501 0.0090 61 0.0013 0.062 0.0068 62 0.0172 0.0658 0.0090 62 0.0012 0.0669 0.0068 63 0.0096 0.0892 0.0090 63 0.0014 0.0612 0.0058 64 0.0104 0.0132 0.0101 64 0.0012 0.0655 0.0048 65 0.0114 0.0739 0.0090 65 0.0015 0.0623 0.0061 66 0.0130 0.0324 0.0090 66 0.0016 0.0615 0.0061 67 0.0094 0.0282 0.0090 67 0.0012 0.0611 0.0048 68 0.0099 0.0258 0.0090 68 0.0014 0.0595 0.0052 69 0.0155 0.0377 0.0101 69 0.0013 0.0582 0.0048 70 0.0093 0.0616 0.0079 70 0.0013 0.0707 0.0052 71 0.0139 0.0211 0.0113 71 0.0011 0.064 0.0055 72 0.0128 0.0270 0.0090 72 0.0011 0.0595 0.0061

179

73 0.0103 0.0341 0.0090 73 0.0011 0.0558 0.0045 74 0.0102 0.0431 0.0079 74 0.0011 0.0625 0.0058 75 0.0115 0.0503 0.0090 75 0.0011 0.0631 0.0048 76 0.0112 0.0415 0.0090 76 0.0018 0.0562 0.0052 77 0.0092 0.0275 0.0079 77 0.0015 0.0662 0.0055 78 0.0108 0.0393 0.0101 78 0.0011 0.0588 0.0048 79 0.0102 0.0214 0.0090 79 0.0011 0.0674 0.0048 80 0.0120 0.0389 0.0079 80 0.0013 0.0644 0.0065 81 0.0105 0.0375 0.0101 81 0.0013 0.0663 0.0048 82 0.0134 0.0299 0.0079 82 0.0011 0.0652 0.0052 83 0.0125 0.0186 0.0135 83 0.0011 0.0586 0.0052 84 0.0162 0.0429 0.0090 84 0.0015 0.0698 0.0048 85 0.0098 0.0221 0.0113 85 0.0012 0.0592 0.0052 86 0.0123 0.0168 0.0090 86 0.0016 0.0701 0.0048 87 0.0107 0.0217 0.0090 87 0.001 0.0613 0.0052 88 0.0108 0.0815 0.0090 88 0.0012 0.0625 0.0052 89 0.0127 0.0545 0.0090 89 0.0011 0.0555 0.0048 90 0.0099 0.0446 0.0090 90 0.0012 0.0618 0.0052 91 0.0143 0.0295 0.0101 91 0.0012 0.0615 0.0052 92 0.0095 0.0320 0.0101 92 0.0014 0.0635 0.0052 93 0.0096 0.0343 0.0079 93 0.001 0.0584 0.0058 94 0.0110 0.0205 0.0079 94 0.0011 0.0619 0.0052 95 0.0128 0.0399 0.0090 95 0.0015 0.0603 0.0048 96 0.0107 0.0793 0.0113 96 0.0011 0.0615 0.0045 97 0.0123 0.0515 0.0090 97 0.0014 0.0604 0.0055 98 0.0145 0.0393 0.0101 98 0.0014 0.0612 0.0058 99 0.0106 0.0346 0.0079 99 0.0012 0.0577 0.0048

100 0.0102 0.1036 0.0101 100 0.0015 0.0678 0.0055 Avg. 0.0117 0.0382 0.0096 Avg. 0.0012 0.0615 0.0052 Real 0.0957 0.0019 0.0419 Real 0.0579 0.0008 0.0473 Ratio 8.1795 0.0500 4.3646 Ratio 48.2500 0.0130 9.0961