A Theory of Viral Growth of Social Networking Sites

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A THEORY OF VIRAL GROWTH OF SOCIAL NETWORKING SITES

MICHAEL T. FISHER

Submitted in partial fulfillment of the requirements

For the degree of Doctor of Philosophy

Dissertation Committee:

Kalle J. Lyytinen, Ph.D., Case Western Reserve University (chair)

Dick Boland, Ph.D., Case Western Reserve University

Jerry Kane, Ph.D., Boston College

Rakesh Niraj, Ph.D., Case Western Reserve University

Toni M. Somers, Ph.D., Wayne State University

Weatherhead School of Management

Designing Sustainable Systems

CASE WESTERN RESERVE UNIVERSITY

May, 2013 CASE WESTERN RESERVE UNIVERSITY

SCHOOL OF GRADUATE STUDIES

We hereby approve the thesis/dissertation of

Michael T. Fisher

candidate for the Doctor of Philosophy degree*

(signed) Kalle Lyytinen (chair of the committee)

Richard J. Boland

Gerald Kane

Rakesh Niraj

Toni Somers

(date) November 29, 2012

* We also certify that written approval has been obtained for any proprietary material contained therein.

ii TABLE OF CONTENTS

Acknowledgements ...... ix

Abstract ...... xi

Introduction ...... 1

Motivation and Outline ...... 1

Literature Review...... 5

Defining Platforms ...... 6

The Nature of Growth on SNS Platforms ...... 8

Co-creation and Co-production Processes on SNS Platforms ...... 13

Individual Motivations for UGC ...... 15

Individual Attitudes towards SNS ...... 17

Two-Sided Markets ...... 21

Theoretical Framing ...... 25

What Is Known ...... 28

What Is Not Known ...... 29

Research Methodology ...... 31

Research Design...... 31

Qualitative Strand ...... 37

Quantitative Strand ...... 38

Overview of Main Results ...... 40

Paper 1: The Co-Production of Viral Growth: A Comparative Analysis of Two Social

Networking Sites ...... 40

Paper 2: The Antecedents of Viral Growth on Social Networking Platforms ...... 41

iii Perceived ease of use...... 43

Page load time...... 43

Co-production...... 43

Self-identity...... 44

Paper 3: The Influence of User Mix on the Viral Growth of Social Networking Sites

...... 44

Summary of Findings ...... 45

Conclusions ...... 46

Discussion ...... 46

Understanding self-identity and social behaviors...... 47

Reciprocity of user generated content...... 49

Differences in platform behaviors and learning...... 50

Social exchange model...... 52

Intrinsic vs. extrinsic...... 53

Implications for Practitioners ...... 54

Misuse or bricolage...... 55

Motivations of users...... 56

Limitations and Future Research ...... 57

Qualitative strand...... 58

Quantitative strand...... 59

Mixed methods research design...... 60

Future research...... 62

iv Appendixes

Appendix A: Alexa Ranking ...... 65

Appendix B: The Co-Production of Viral Growth: A Comparative Analysis of

Two Social Networking Sites ...... 66

Appendix C: The Antecedents of Viral Growth on Social Networking Platforms99

Appendix D: The Influence of User Mix on the Viral Growth of Social

Networking Sites ...... 175

References ...... 225

v LIST OF TABLES

Table 1: Unknowns Mapped to Research Questions ...... 31

Table 2: Quality Criteria ...... 58

Table 3: Model Comparison by Age ...... 64

Table B1: Sharing and Usage Distribution ...... 70

Table B2: Original and Selected Themes ...... 72

Table B3: Social Processes of Selves, Viral Equation, and p-values ...... 90

Table C1: Demographics ...... 126

Table C2: EFA & CFA Summary ...... 130

Table C3: Correlation Matrix...... 133

Table C4: Model Fit Statistics for Structural Analysis ...... 136

Table C5: Hypotheses Summary ...... 139

Table C6: Indirect Effects of Voyeurism and Exhibitionism on Fan Out and

Retention ...... 143

Table C7: Summary of Model Differences ...... 144

Table C8: Model Comparison ...... 145

Table C9: Control Effects ...... 146

Table D1: Within User Voyeur to Exhibition Ratio ...... 186

Table D2: Between Users Voyeur to Exhibition Ratio ...... 186

Table D3: Demographics ...... 197

Table D4: Summary of Clustering Models for Within Users ...... 199

Table D5: Clustering for Voyeurism & Exhibitionism within Users ...... 201

Table D6: Summary of Clustering Models for Exhibitionism between Users ...... 201

vi Table D7: Summary of Clustering Models for Voyeurism between Users ...... 201

Table D8: Clustering Between User ...... 202

Table D9: EFA & CFA Summary ...... 206

Table D10: Correlation Matrix ...... 208

Table D11: Hypotheses Summary ...... 213

Table D12: Voyeurism & Exhibitionism for Networks and Users ...... 214

Table D13: Interaction of Groups (Sample Size) ...... 215

Table D14: Interaction Findings ...... 215

Table D15: Interactions R2 ...... 215

Table D16: Post-hoc Analysis within Users ...... 216

vii LIST OF FIGURES

Figure 1: Viral Growth Equation ...... 11

Figure 2: Overall Research Design ...... 34

Figure 3: Triangulation Model ...... 35

Figure 4: Structural Model ...... 43

Figure B1: Contrasting Perceptions of Organization Culture & Product Development

Focus ...... 75

Figure B2: Viral Coefficient of Facebook ...... 87

Figure B3: Conceptual Model of Viral Growth ...... 94

Figure B4: Observation Counts of Social Processes Graphed By Selves ...... 97

Figure B5: Two-Dimensional Scaling of Social Processes ...... 98

Figure C1: TAM Model of Viral Growth (Fan out / Retention) in Digital Platforms .... 107

Figure C2: Social Exchange Model of Viral Growth in Digital Platforms ...... 111

Figure C3: An Integrated Model of Viral Growth ...... 119

Figure C4: Final Structural Model Direct Effects ...... 135

Figure C5: The Influence of Voyeurism on Fan Out ...... 140

Figure C6: The Influence of Exhibitionism on Fan Out and Retention ...... 141

Figure D1: Hypothesized Model ...... 187

Figure D2: Four Cluster Model Profile Plot ...... 200

Figure D3: Cluster Plot ...... 212

viii ACKNOWLEDGEMENTS

Nothing of significance is ever achieved alone. For every person on top of a mountain or onstage receiving an award there are a host of individuals that have guided, supported, coached, and mentored along the way. My journey has been no different and there are many people to whom I am indebted. My own limitations do not permit me to acknowledge every individual personally but there is a special group that I must thank by name.

I would like to express my sincere gratitude to the chair of my dissertation committee, Kalle Lyytinen. He has played many roles in my journey including advisor, professor, colleague, coauthor, and friend. He has demonstrated great patience and professionalism in all situations, setting the standard in many regards to which I aim. I look forward to continuing to work, collaborate, and learn from him.

I would also like to individually thank each member of my dissertation committee

– Toni Somers, Richard Boland, Jerry Kane, and Rakesh Niraj. Toni with her mix of coaching, encouragement, and instruction has been invaluable. She is a deep well of knowledge and a constant source of inspiration. Dick possesses a gifted mind for connecting ideas and concepts in new and interesting ways. He was the inspiration for many of the theoretical underpinnings that proved to be amazingly fertile grounds for explaining great portions of the phenomenon that we were investigating. Jerry with his quick wit and keen insights has helped guide not only this dissertation but much of our thinking about how to present our ideas to a broader audience. Rakesh has proved to be a valuable member of the committee in many ways including his insightful feedback on several critical concepts.

ix I have had the pleasure of knowing Sue Nartker and Marilyn Chorman for the past fifteen years and have benefited from their support and encouragement over most of that time. I am very grateful for everything they have done to aid me in my academic pursuits.

Most important, I would like to express my deepest gratitude to my family for being the source of constant inspiration and support. To my parents, Robert and Shirlee, who instilled in me the highest regard for academics, I am grateful for the love of learning that this has inspired. Regarding my other parents, Ron and Laurie, who have been the best surrogate cheerleaders one could ever ask for, I very much appreciate their presence in my life. Heroes can come in any shape or size and my son Spencer is one in my eyes. I often draw inspiration from his courage, enthusiasm, and kindness. I strive to live up to his expectations and set an example for him with regard to dedication, resilience, patience, and stoicism. To my wife, Ashley, my biggest fan, I could not do much of what I do without your support. You have enabled me to reach for the stars by providing the bedrock on which to stand.

Last, I must not forget all the individuals who have gone before me. As Isaac

Newton wrote in a letter to Robert Hooke in 1676 “If I have seen further it is by standing on ye sholders of Giants”. Many scholars and practitioners have gone before me clearing the path, allowing me to find my way to the edge and push just a little further onward.

A Theory of Viral Growth of Social Networking Sites

Abstract

MICHAEL T. FISHER

Social networking platforms, systems designed to provide digital content services

specifically for social network sites (SNS), continue to develop through a rapid

combination of components forming a service ecology that is much more than a single

tool or service. These SNS have experienced tremendously rapid growth rates and

traditional economic factors put forward to explain growth such as pricing are

inadequate. Explanations offered by platform scholars for the exponential growth of SNS

such as Facebook do not go far enough in explaining why some platforms such as

Facebook grow while others such as Friendster do not, despite following somewhat similar growth strategies advocated in the literature.

In this thesis I develop a theoretical model that offers greater power and detail than previous models – that focus on single user-tool technology adoption – in explaining the growth of SNS. It builds upon the work on two-sided economic models but seeks to expand them using social exchange theory to situations where the exchanged value is not monetary. The dissertation covers the motivation, prior research, theoretical foundations, research methodology, findings, and contributions. Following mixed methodology I

xi utilized a grounded theory approach by first conducting semi-structured interviews with

technology executives and users of two SNS that have experienced dramatically different

growth patterns to identify and explain user related behaviors that drive growth. Informed

by this study, I next hypothesize a research model that draws upon platform processes of

co-creation and co-production as well as user features of voyeurism and exhibitionism to

explain SNS growth – measured by fan out and retention. The model posits that the

growth of SNS is mediated through the participation in the co-creation and co-production processes. In a second study, I analyzed to what extent the ratios of user propensity towards either voyeuristic or exhibitionistic behaviors affect the fan out and retention of

SNS. To validate my theory, I tested the models with survey data from 1449 users of eight different SNS using clustering techniques and structural equation modeling.

The thesis makes several theoretical, methodological, and practical contributions to research on technological innovation diffusion and the growth of two-sided markets.

Service-dominant logic models have typically predicted that co-production is a component of co-creation and in contrast I demonstrate a chained mediation through co- creation to co-production for the construct of retention on SNS. I provide support for the technology adoption theory with a focus on multi-user, multi-technology contexts such as

SNS platforms and amend the explanations with additional individual and platform constructs as to improve its predictive power of technology adoption as examined through the lens of viral growth.

Key words: Social networking sites; co-creation; co-production; voyeurism; exhibitionism; social exchange

xii INTRODUCTION

Motivation and Outline

Information technologies previously viewed as isolated systems are increasingly

seen as ‘platformized’ components of infrastructures that must offer a changing variety of services (Mathiassen & Sørensen, 2008). One class of such platforms – called social networking sites (SNS) – and their growing variety of services have been instrumental in building and sustaining new types of social networks over the Internet. Overall, SNS services are geared towards using digital information and its manipulation on the platform to identify, record, represent, and facilitate relationships among individuals by sharing, organizing, and manipulating user generated content (UGC) about individuals, their common activities, or interests. These networks, in turn, can be defined as “a social structure of nodes that represent individuals (or organizations) and the relationships between them within a certain domain” (Liccardi et al., 2007: 225). Social networking sites overall exemplify a new type digital platform technology as digital platforms SNSs , must include a set of stable components that support a continual assortment of services with the ability to morph into new services (Baldwin & Woodard, 2009). Therefore, SNS

platforms are typically founded on an open organizing architecture that has the capability

to integrate services, processes, and technologies in ways that enable classes of social

interactions and promote their growth.

Because of their rapid growth, popularity, and at times astronomical monetary

valuations, SNS have attracted the attention of researchers and practitioners interested in

understanding everything from the potential gains and harms stemming from the use of

SNS services (Hempel & Kowitt, 2009) to the building and maintenance of social capital

1 (Ellison, Steinfield, & Lampe, 2007). The rapid growth of SNS platforms has kindled a

keen desire to understand platform evolution and growth and their antecedents

(Chesbrough & Spohrer, 2006). This has invited over the last decade multidisciplinary efforts to characterize the nature, positioning, diffusion, and effects of platforms

(Parameswaran & Whinston, 2007a; Rai & Sambamurthy, 2006) and generated a

growing stream of research in networks economics (Van den Bulte, 2010; Van den Bulte

& Lilien, 2001; Van den Bulte & Stremersch, 2004) and social network studies

(Abrahamson & Rosenkopf, 1997; Enders, Hungenberg, Denker, & Mauch, 2008;

Hamm, 2008) on factors that promote digital platform growth. The bulk of these studies

address how platforms enable scalable economic exchanges and thereby generate positive

network effects (both direct or indirect) that will drive platform growth (for a review see

Baldwin & Woodard, 2009; Boudreau & Hagiu, 2009; Evans, 2009; Gawer, 2010).

The exchange of economic value, however, is not the primary driver for use on

SNS – their use is free and primarily facilitates exchange of personal or social information and related experiences in a “mosaic of social worlds” (Boudreau & Hagiu,

2009). Therefore, traditional economic factors put forward to explain growth such as pricing are inadequate. Therefore explanations offered so far by platform scholars for the growth of SNS do not currently go far enough in explaining why some platforms such as

Facebook grew fast while others such as Friendster did not, despite following largely similar economic strategies of subsidizing one side completely by offering free services.

In summary, researchers lack explanations that look beyond pricing or economies of scale or scope and instead consider the actual behaviors of the users in promoting the

2 growth, the nature and content of offered services, and the way they are developed by the platform provider.

In this study, I will examine these three issues: 1) the nature of user behaviors promoting growth; 2) the nature and content of services that invite appropriate growth promoting behaviors among users; 3) the ways in which platform providers should develop their services in order to promote this growth.

In addressing the first topic, I will examine the foundations of social exchange theory (SET) and individual traits that promote exchange of UGC. SET, initially introduced in the 1950’s (see Blau, 1964; Homans, 1958; Thibaut & Kelley, 1959), has been applied to a diverse set of research topics including social power (Molm, Takahashi,

& Peterson, 2000), organizational networks (Brass, Galaskiewicz, Greve, & Tsai, 2004) and leadership (Liden, Sparrowe, & Wayne, 1997). Normative rules, that define proper behavior by participants in an exchange (Emerson, 1976), and the economic value of the exchange are two fundamental concepts within SET. One of the most fundamental rules is that of reciprocity, where participants expect that if they share a particular level of intimacy that they will receive a similar level in return.

In addressing the second and third issue, I will draw upon recent research on service-dominant logics of value exchange (Vargo & Lusch, 2004). These logics form a radical departure from a goods-dominant logic, and argue that value is created with and determined by customer (user) behaviors during the creation and consumption phases

(Lusch & Vargo, 2006b). Accordingly, in this study of SNS I need to be concerned with the antecedents of user involvement in the constant creation of UGC (co-creation) and in

3 ‘evolving’ of the platform services (co-production) that promote further growth of the

platform.

Co-creation is defined as allowing users to “actively co-construct their own consumption experiences through personalized interaction” (Prahalad & Ramaswamy,

2003: 1), as well as “activities conducted by consumer communities or by individuals at the behest of an organization (characterized as producers-including platform producers, e.g., Facebook)” (Zwass, 2010: 11). SNSs in general can be viewed as platforms that offer venues for co-creation processes that permit users through the exchange of UGC to project their identities and related information in the form of pictures, comments, tags and likes and dislikes in the brave, dynamic, and free floating digital world. Co- production is a parallel and constant process on a SNS platform where users actively contribute to and select new elements that will be included in the next round of service

offerings on the SNS (O’Hern & Rindfleisch, 2009:4). Hence, through co-production

processes SNS users become directly involved in cycles of service design, production,

and marketing based on their positive or negative experiences of using the service, new

ways of using the service (misuse), or suggestions for new types of services (Schultze &

Bhappu, 2005). Currently, there are no studies that have examined the effects of co-

creation and co-production on service growth of SNS. Nor, do researchers know what

drives the growth of such processes.

To advance understanding of these three issues surrounding the growth of SNS,

the research questions that I set out to address in this thesis are:

1. What underlying causal mechanisms, in terms of how users exchange UGC and innovate on the platform, produce viral growth among SNS?

4 2. To what extent do individually based attitudes explain viral growth versus factors that drive social exchanges related to co-creation and co-production on a SNS? What other possible technological, individual, or social antecedents will drive these processes?

These two questions investigate the key non-economic factors influencing the growth of the SNS platforms. They focus on drivers that explain the exchange of UGC within the user-user dyad. The explanation draws upon the structure and symmetry / asymmetry arguments underlying economic models of two-sided markets, but expands the logic of participation on those markets beyond economic rationale. In line with Nass and Moon (2000) I posit that the ‘economic’ exchange on a SNS is primarily driven by

“reciprocal self-disclosure” where users who receive intimate disclosure feel obligated to respond with an equal intimate disclosure. The remainder of this introduction is as

follows. First, I review previous research of digital platforms, viral growth, co-creation,

and co-production by focusing on the past multi-sided market explanations for platform

growth. Next, I discuss the theoretical framework that I articulated to study growth promoting user behaviors on digital platforms. Next, I outline the methodology and its alignment to the research aims and theoretical foundation. Lastly, I conclude with a

summary of the remaining chapters of this dissertation.

LITERATURE REVIEW

In this section I will review the concept of a platform, digital platforms, and probe

how extant literature that has sought to explain platform growth. I will also summarize

findings emerging from studies of multi-sided markets and networks effects followed by

a review of the viral growth equation. I will end with a short summary of past research that identifies the need for individual and socially related theories of platform growth.

5 Defining Platforms

Combinative and open capabilities of IT have recently resulted in

‘platformatization’ of previously isolated applications. As a result these new types of platforms offer now a wide range of (digital) services – often in the form of freely exchanged UGC in organized communities such as social networking sites or open source software (Mabillot, 2007; Tilson, Lyytinen, & Sørensen, 2010; Tiwana & Konsynski,

2010). The complexities of these social network platforms require significant augmentation in explaining their adoption and use patterns when compared to the past single user/single technology adoption models.

Recently, Venkatesh (2006) suggested three avenues for future IT adoption research: business process change, supply-chain technologies, and services. As a result, a growing body of research has focused on the digital services and related economics of platforms that drive adoption and user value (Baldwin & Woodard, 2009; Boudreau &

Hagiu, 2009; Evans, 2009; Gawer, 2010). While the concept of a platform in these studies is relatively broad, it has been developed by management scholars in three overlapping streams of research: products, technological systems, and transactions

(Baldwin & Woodard, 2009). The generic term ‘platform’ has been adopted to characterize broad classes of products, services, or institutions that mediate transactions or exchanges between different markets or communities (Rochet & Tirole, 2003).

My definition of a social network site as a platform conforms to this broad characterization: SNS is after all an evolving and open IT system designed to provide a variety of UGC related services that represent individuals (or organizations) and the relationships between them within a certain domain. With this definition I concur with

6 Mak (2008) that software products distributed on tangible devices such as CDs or DVDs

are considered “goods,” whereas digital UGC distributed over the Internet are considered

“services” – such as e-books (Hillesund, 2007), and online videos (Oh, Susarla, & Tan,

2008). Overall, SNS services use general characteristics of digital information (Oinas-

Kukkonen, Lyytinen, & Yoo, 2010) on the platform to identify, record, represent, and facilitate relationships between individuals by sharing, organizing, and manipulating

UGC about these individuals, their common activities, or interests.

These new open platforms have resulted in UGC becoming one of the primary sources of news and entertainment (Thurman, 2008). Despite recent advances in the understanding of social capital as a motivator for technology usage (Ellison, Steinfield, &

Lampe, 2006; Ellison et al., 2007; Steinfield, Ellison, & Lampe, 2008) there is a fundamental gap in knowledge about the motivations of users to generate content. With the significant drop in prices of technology for creating digital content and the diffusion of skills to use it, people now can use the internet as a primary read-write medium (Fonio,

Giglietto, Pruno, Rossi, & Pedrioli, 2007). Above, I referred to this partly as user related co-creation of content. This is built upon service platforms such as Wikipedia and del.i.cious, where freely exchanged and co-created content is organized around communities of simultaneous content creation and consumption (Mabillot, 2007).

Content based communities and related platforms have also been among the largest and fastest growing parts of digital services. As Cuff, Hansen and Kang (2008: 29) assert,

“the ways data (content) is structured and shared are fundamentally different than they were a decade ago.”

7 The growth of SNS must contend with the chicken-and-egg problem posed to any platform builder and recognized for some time in economics research in terms of network effects: How does one get the users to exchange UGC value that content, if there is no such content on the platform in the start? The platform can only deliver value to

‘demand’ side of the platform (UGC consumption), if it has participants also on the other supply side (UGC supply). Why would one shout in a digital forest if no one is listening?

The key challenge for a platform provider to generate growth is how to make content creation and consumption meet (Evans, 2009). Thus, SNSs as platforms are inherently multi-sided. They must support multiple simultaneous social exchanges across dyads which I call user-user dyads. It is this dyad here where individuals enter to exchange

UGC and which in the end determines the value of the network services to the users (both as suppliers and consumers of UGC). Economic, transaction focused models of growth do not work well in explaining the growth of such UGC taking place in user-user dyads.

Indeed, the value of such exchanges comes primarily from forging social bonds, generating social awareness (gossip), and offering capabilities to create social identity

(projecting who you are and want to be into the digital world). Hence, there is an urgent need for research that focuses on the nature and dynamics of social exchanges on SNSs and which thereby can explain the growth UGC, the growth of services and ultimately the growth in the number of users and use. The first thing in addressing this challenge is to understand the nature of growth and what drives it in social networks.

The Nature of Growth on SNS Platforms

According to economic models of many sided markets when positive network effects kick in the platform is likely to exhibit a fast (exponential) growth pattern, This

8 growth is often equaled to that of a spread of a contagious virus in a population.

Therefore a widely used term to characterize observed growth is an epidemiological term

of viral growth (Penenberg, 2009).

Though in everyday life most people do not intentionally spread viruses, they can do it unintentionally by spreading in their social network information about new services, new features, benefits of services, or just telling about themselves or others on a SNS

(Jurvetson, 2000). The resulting information spread when depicted visually is similar to a virus spread in a population. The spread follows a power law distribution until the product reaches a point of non-displacement (Penenberg, 2009). Because of well-defined structural conditions that determine such growth the rate of growth can under certain conditions be accurately predicted. This requires acquiring and estimating information of the factors that affect the spread (conversion) and the retention (how many will remain affected) as to predict “contagion” in the population. Because many of these factors can be influenced by strategies and tactics followed by the platform provider understanding what factors and how influence the contagion should be a keen focus of the digital platform provider, if he or she seeks viral growth (Jurvetson, 2000).

The first factor – contagion – deals with the rate at which new users become

“converted” to platform users. The growth is determined by the extent to which current users send requests to their friends or others to participate in a service and whether those individuals ‘convert’ and become users as well. To describe the rate of this process,

Kalyanam coined the concept of a Viral Index (2007) or Viral Coefficient (Penenberg,

2009) that predicts how quickly viral growth can occur for a service provider. The Viral

Coefficient (Cv) is a function of the Fan Out (number of new platform users invited per

9 existing user) multiplied by the Conversion Rate (number of new platform users converted to using the service) at any point of time is defined as:

(1)

Clearly, Cv must exceed 1.0 to generate viral growth. Fan Out can be influenced

through provider mechanisms such as, sponsored marketing or by enabling users to share

recommendations and links with potential new users through a process dubbed a “social

cascade” (Cha, Mislove, Adams, & Gummadi, 2008). Conversion rate is determined by

the intrinsic ‘hedonism’ a user obtains from the process of using the service and the effort

required to begin using the service (Penenberg, 2009). It is affected by factors such as the

perceived value, learning effort (ease of use) (Tedeschi, 2000), service quality (Kuan,

Bock, & Vathanophas, 2005) and perceived entertainment (Kim & Forsythe, 2007).

Although Cv measures service growth over time, it cannot identify a sustainable

growth strategy for a platform provider, because it fails to measure sustained growth,

growth that takes into account a loss of users to competitor’s new services, or users

dropping the service because of loss of interest or value. To be beneficial by increasing

the number of Cumulative Users over time, a provider must know to what extent it can

retain those users who have already adopted a service. This is achieved by multiplying

the Cv by Retention Rate and raising the product to the exponential of the Frequency

(number of times the service is used per cycle i.e. intensity of social exchanges) and

Length of the Cycle, (i.e. fixed time period e.g., 1 day, 1 week, 1 month which depends on the feature and nature of social exchange) (Jurvetson, 2000). Thus:

(2)

10 Further, the number of Cumulative Users are based on different values for the

Viral Coefficient (see Figure 1). If viral coefficients are below 1 the exponential growth

is not possible. In this example, where the Frequency is 1, it is shown that even with a

Retention Rate (R) of 100% but with only a Cv of 0.9, there is not viral growth. With only a 70% Retention Rate but with a Cv of 1.2, viral growth can be achieved.

FIGURE 1: Viral Growth Equation

The number of cumulative users can be improved on a platform in several ways.

That is, the cumulative growth is a) affected by the number of new users joining (higher

viral coefficients lead to higher number of cumulative users), and b) the number and

quality of services offered (i.e. to what extent the platform provider innovates and offers

broad range of services of interest to the users). Platform characteristics such as service

breadth and service quality clearly increase retention rate (Chen & Hitt, 2003). Broad

horizontal applications, for example, e-mail has also been found to be predictors of

11 increased frequency of usage leading to higher retention rates (Emmanouilides &

Hammond, 2000). Thus, the cycle of creating cumulative growth depends on the nature

and scope of the services offered – the more often the services are frequented, the higher

the cycles.

Additionally, there are many ways suggested by the viral equation in which a

platform provider can succeed or fail to achieve viral growth. If the adoption rate is not

sufficiently high (driven by fan out or intensity), or if existing users are leaving faster

than they can be replaced (driven by retention rate), viral growth will not result.

Moreover, the nature of services and the length of the frequency of the cycle affect the

service growth. Accordingly, platform providers can utilize many strategies to increase

the viral coefficient, most of which can be categorized under viral marketing, or can

innovate with new features and services on the platform that increase scope and intensity

of user experience thereby affecting conversion, retention, and cycle or frequency. This,

in turn, requires investing in processes through which service renewal and innovation

take place quickly (called co-production above).

In innovation of diffusion parlance, three critical factors that influence viral

growth – 1) fan out (diffusion), 2) conversion (adoption), and 3) retention (continued use

and assimilation). Fan out and retention represent user behaviors exhibited by an engaged

‘converted’ user, while conversion is a behavior exhibited by a potential user. Next, I will focus on explaining the behaviors of active users i.e. what factors influence fan out and retention as these are the ones who most like influence the potential of growth: you need to get new potential users on the site and you need to keep them on the site once converted. If this does not happen ‘conversion’ is not possible. I admit also that the

12 conversion rate is critical in generating cumulative growth but that is also the behavior

which is most researched and relatively well explained by existing adoption theories

(Penenberg, 2009). In contrast, as pointed out above, researchers have neglected to

explore what individual, social and technological factors drive fan out and retention. Nor

do they know what underlying causal mechanisms in terms of how users exchange UGC

and innovate on the platform influence fan out and retention? I will next review social

processes that can to explain those factors. In particular I will review the service-

dominant logics of value exchange (Vargo & Lusch, 2004) as a way to explain constant creation and expansion of UGC.

Co-creation and Co-production Processes on SNS Platforms

Lusch and Vargo (2006a) distinguish between the co-creation of value, which takes place in the consumption phase, and its co-production, which takes place during the design phase. There are two components of collaboration, the co-creation of value and the co-production of the product in which users are actively involved “in the creation of the core offering itself” (Lusch, Vargo, & O'Brien, 2007: 7). Co-creation allows individual customers to co-construct, through personalized interactions, their own experiences of consumption and the unique value is in the co-creation experience itself

(Prahalad & Ramaswamy, 2003). Co-production, instead, is commonly construed as

involving customers in roles typically played by product managers, such as determining

service features. Co-production is thus directly involved in the product or service

development cycle (Schultze & Bhappu, 2005). The value of co-production is that

ultimately the service offering contains desired attributes, whereas the value of co- creation is experienced during service consumption.

13 The process of service development and consumption, in which both co-creation

and co-production take place, is a sequence of operational activities linked in a network

chain and supported by a set of platform features (Achrol & Kotler, 1999). Each activity

– Design and Development, Production, Marketing and Consumption – leads to the next

(Porter, 1998) in a set of simultaneous cycles carried out by multiple actors as enabled by

the platform. Here, the degree of co-production is viewed as a function of both scope −

i.e. propensity of actors to collaborate with customers across all phases of the service

development cycle − and intensity the extent to which actors rely on collaboration within

a particular stage of customer involvement (Hoyer, Chandy, Dorotic, Krafft, & Singh,

2010). Similarly, the degree of co-creation is a function of scope – the fan out of content

being exchanged on the platform – and intensity − the extent and frequency to which

users create or consume content on the platform. An example of both co-creation and co-

production is the emerging form of social production − which Benkler (2006) calls peer

production where large numbers of people collaboratively contribute to design and

produce services or products.

Physical goods primarily emerge from a structured development process, whereas

due to their malleability digital services are developed ad hoc and iteratively (Gustafsson

& Johnson, 2003). This has led to a growing trend of involving customers as a co-

producer of digital services (Davidow & Malone, 1992; Etgar, 2008; Grönroos, 1993;

Hoyer et al., 2010) and a spate of research that seeks to measure the extra value such

involvement provides (Wikström, 1996). As a result, the traditional view of company- centric value creation is becoming obsolete in favor of one in which the role of the consumer has changed from being passive to active (Prahalad & Ramaswamy, 2004).

14 Indeed, customers of today have become so intimately involved in the development and usage of services that in many cases they have become co-creators of value (Kambil,

Friesen, & Sundaram, 1999). This is a shift from creating value for the customer to creating value with the customer across the whole life cycle of services whereby the knowledge and resources possessed by the customers are complemented by those of the service provider (Normann & Ramirez, 1993; Wikström & Normann, 1994). As a result, firms seek increasingly to benefit from the process of co-production and view it as a way to achieve sustainable competitive advantage (Prahalad & Ramaswamy, 2004), anticipating that co-production can add new value to a service (Wikström & Normann,

1994) because of the wealth of finely segmented demand information held by a user community (Parameswaran & Whinston, 2007b). Similar to Zwass (2010), I believe that a broad array of motivators explain the creation and consuming of UGC . Moreover, these are different from a set of factors used to explain participation in the exchange of economic value. The latter process deals with maximizing utility and/or minimizing cost, whereas the former relates to motivators that influence participation in social exchanges that create reciprocity and value like social bonding, and the projection of self-identity.

The positive experiences of engaging in the service derive from the acts of co-creation will likely reinforce the user’s intention to recommend the service to friends, family, and colleagues (fan out) and to revisit regularly to use the service (retention). Next I will review some of the individual level features that can explain growth on a SNS.

Individual Motivations for UGC

On most platforms offering UGC the nature of which is highly dynamic and fluid and never controlled by any single user or actor. This lack of control is, as Munar (2010:

15 418) notes, “part of the essence of this virtual sharing of information, and … part of the

system architecture of the social network sites.” The increasing prevalence of webcams

as well as cameras in game stations and phones now allow the recording and presentation

of individuals’ daily lives across the internet, changing the conventional code of what can

or cannot be shown and to whom (Koskela, 2002). It has been demonstrated that the architecture of digital service platforms and the culture of online communities promote

emotional exhibitionism (Munar, 2010) and related voyeurism, with individuals

projecting their identities (sometimes fictitious) in the dynamic and free floating digital

world and offering opportunities for others to peep in and examine them. Hence, how the

UGC in these communities is positioned in promoting value creating social exchanges is

determined to a large extent on whether they either reveal facets of “I” to a growing

number of others, or allow ”I” to see “you” or the “other” in new ways. Consumers

demonstrating exhibitionism, for example, upload pictures, post comments, and update

statuses in the hope that others will view and interact with their displays. Customers

exhibit voyeuristic behavior when they access this content and engage in new social

exchanges about it. As a result, the character of UGC is vastly different from the

producer centered content of traditional media, which largely follows the economic

production logic of multi-sided markets (Jones, 2010).

Motivations for exhibitionism on SNS include self-validation, the desire to

manage one’s self-identity, the development of new relationships, and the desire to exert

social control (Calvert, 2004). As Jones (2010: 262) notes, this form of exhibitionism “is inherently more authentic and thus more intimate than producer-generated content.”

Nakamura (2002) explains it by asserting that the physical body is irrelevant in a digital

16 environment, and individuals are thus free to construct their own likeness, becoming

“entrepreneurs of the self.”

Digital exhibitionism, in turn, invites the development of ‘digital’ voyeurism which further challenges the division between the public and the private (Munar, 2010).

The term mediated voyeurism has been introduced to reflect the consumption of

“revealing images of and information about others’ apparently real and unguarded lives…not always for purposes of entertainment but frequently at the expense of privacy and discourse, through the means of the mass media and Internet” (Calvert, 2004: 2).

The joint dynamic of mediated exhibitionism and voyeurism relies on and spawns the constant expansion of UGC. The need to expand this content has also lead to an insight that the growth in UGC can only be achieved through contagious viral growth by affecting the scope and intensity of social exchanges. While this offers an explanation for the individual’s motivations for participation in the viral growth process, this does not indicate what technical features are enabled by the SNS platform to encourage or thwart these behaviors. I will discuss these next.

Individual Attitudes towards SNS

Technology Acceptance Model (TAM) is one of the most widely-employed individual level models of explaining acceptance and use of computing technologies. The model was initially developed and tested in the 1980s in the context of explaining use of individual tools such as spreadsheets (Davis, Bagozzi, & Warshaw, 1989; Venkatesh &

Davis, 2000). Subsequently, the model has been extensively validated across a variety of computer use settings while being subjected to significant theoretical extensions and refinements (Venkatesh, Morris, Davis, & Davis, 2003). For example, Venkatesh and

17 Davis (2000) proposed TAM2, an extension to TAM, that theorized the general

determinants of perceived usefulness – subjective norm, image, job relevance, output

quality, result demonstrability, and perceived ease of use – and two moderators –

experience and voluntariness. Venkatesh and Bala (2008) proposed TAM3 as an

integrated model of technology acceptance representing the determinants of individuals’

information technology adoption and use. TAM3 was constructed by combining TAM2

with the model of the determinants of perceived ease of use (Venkatesh & Davis, 2000).

In this context it has received substantial empirical support (Adams, Nelson, & Todd,

1992; Agarwal & Karahanna, 2000; Venkatesh, Davis, & Morris, 2007; Venkatesh et al.,

2003) been found to consistently explain around 40% of the variance in individuals’

intention to use and a significant portion of actual usage of information technology

(Venkatesh & Bala, 2008). Overall, the line of TAM models suggest that perceived

usefulness and perceived ease of use are central beliefs about technology being

introduced that will influence an individual's (positive) attitude towards the technology

and therefore predict its use (Davis, 1989; Davis et al., 1989). These have often been

amended with personal level factors such as self-efficacy or social factors such as group

pressure or norms (Venkatesh et al., 2003). Due to the generic nature of these factors

strong criticisms have been leveled against the model’s lack of “actionable guidance” of

how to promote technology adoption in specific situations (Lee, Kozar, & Larsen, 2003).

As a response, researchers have sought to introduce context-specific antecedents to the

TAM including e.g. e-mail features (Karahanna, Straub, & Chervany, 1999) and e- commerce features (Koufaris, 2003). Therefore further refinements of such antecedents by integrating the general determinants of perceived usefulness and perceived ease of use

18 appear to also hold the key in explaining the adoption of services associated with digital

SNSs (Delone & McLean, 2003; Rai, Lang, & Welker, 2002; Venkatesh & Bala, 2008).

The practical utility of considering TAM in explaining SNS growth stems from

the fact that SNS platforms are fully technology-driven and considered as a new type of

innovative technology that each individual user must adopt and appropriate. Thus, it is

fitting also to explain not only conversion, but also other growth factors on the SNS

platforms – fan out and retention. Thereby, antecedents that occupy the nomological

structure of TAM – perceived usefulness and ease of use – are posited to predict also fan

out and retention behaviors. Since these user behaviors originate from technology use, it

is reasonable to expect that the variables included in the TAM can be used to predict

associated use behaviors. This is not new, either. For example, Gefen and Straub (2000)

examined the effect of perceived ease of use on e-commerce acceptance, while Moon and

Kim (2001) examined the effect of perceived usefulness and ease of use on consumer use of the Internet.

Consumer’s perceived risk of online privacy and security has been shown to deter use of ecommerce services unless they perceive the service as a reliable milieu. Research reports a positive relationship between consumers’ levels of internet experience, perceived risks, and online activity as crucial elements of e-transaction (Miyazaki &

Fernandez, 2001). The presence of an Internet security and privacy statement or an indication the site is a trusted source have been found to increase anticipated disclosure and patronage rates for consumers with relatively high online shopping risks (Miyazaki &

Krishnamurthy, 2002). Finally, researchers have found that the presence of a clear and simple privacy policy increases the users’ of digital platforms participation in co-creation,

19 co-production, as well as their perception that the platform is useful (Fisher, Lyytinen, &

Somers, 2011). Therefore I can posit that privacy and security also have an effect on fan

out and retention rates.

It has been shown that even the slightest slowness in the download of web pages –

even as little as 400ms latency – significantly reduces the number of user’s Google searches (Brutlag, 2009). Similar findings have shown reduced ecommerce site browsing when delays exceeded 4 seconds (Galletta, 2002). Therefore I also assume that technological features of download time will influence fan out and retention.

The impact of the clear presentation of system features on perceived ease of use

has been well documented (Benbasat, Dexter, & Todd, 1986; Dickson, DeSanctis, &

McBride, 1986). Users will attend to different aspects of the display in different ways and

“traffic and sales are adversely influenced by poor interface features” (Lohse & Spiller,

1998:1). Users want a “simple, clear interface” (Mayhew, 1999) thus increasing the perceived ease of use. Overall it can be expected that user friendliness of the site will influence fan out and retention.

I have summarized the literature surrounding individual, social and technological

factors that can drive fan out and retention on SNS platforms. I have also investigated

how a platform provider gets users to exchange and value UGC when the platform can

only deliver value to one side of the platform, if it already has participants also on the other side. This suggests that SNS platforms are inherently multi-sided in the sense that they consist of multiple social exchange based dyads. However, the value of social exchanges comes primarily from the creation of social bonds, awareness, and capabilities

20 to create social identity as opposed to monetary transactions. Therefore it is important to

also review what is known about exchanges on multisided ‘markets’.

Two-Sided Markets

Two-sided or multi-sided markets are markets in which a platform enables

interactions between buyers and sellers, and where platform providers can charge them for the service. As Rochet and Tirole (2006) explain, “platforms court each side while attempting to make, or at least not lose, money overall.” Examples of two-sided markets abound in the literature including videogame platforms, TV networks, newspapers, and payment cards (Armstrong, 2006; Rochet & Tirole, 2003, 2004, 2006). Two-sided markets are important in the analysis of digital platforms as platforms create multi-sided interactions between several actors on the platform: 1) users and advertisers (co- valuation, such as Google sales of adds based on the search content), 2) developers and platform providers (co-production, such as game developers developing games for

Facebook), or 3) users and users (co-creation; such as users uploading content on

YouTube). Hence understanding what drives growth in such two sided or many sided markets is of utmost importance in analyzing the evolution growth of digital SNS platforms. This also includes a necessity to understand differences and similarities between these multi-sided markets and also dynamics of their co-evolution.

Any two-sided digital platform enabling two-sided markets must follow growth dynamics of co-valuation, where economic value is offered to both buyers and sellers who join the market (Rysman, 2009). It is primarily driven by the direct and indirect positive network externalities in either or both sides of the markets (Parker & Van

Alstyne, 2005). This kind of two-sided market, under certain conditions, catalyzes

21 virtuous feed-forward cycles that trigger exponential growth; wherein more demand from

the user group spurs more interest from the advertisers (Eisenmann, Parker, & Van

Alstyne, 2006). Transaction-oriented service platforms such as eBay and Amazon

accordingly focus on growing and tightening buyer-seller dyads as the main factor that

spurs the platform growth, as it increases the number of transactions run on the platform

as well as the number of sellers and buyers being matched. In this situation, the economic

nature of transactions is also clear – viz. users as are viewed in the role of sellers and

buyers – and these roles and related processes are placed on the foreground during the

platform use. Other processes e.g. rating systems (Resnick & Zeckhauser, 2002) run in

the background as support processes: though the users are aware of and make use of these

processes, they are not the primary raison d’être of the platform.

The majority of literature on two-sided markets has focused on such buyer-seller

dyads and related economic incentives to join the platform (see Curien & Moreau, 2007;

Evans, 2003; Evans & London, 2008; Evans & Schmalensee, 2005; Palmatier, Dant,

Grewal, & Evans, 2006; Rochet & Tirole, 2003, 2004). Accordingly, they use economic models to explain why digital service providers can attract user “eyeballs” and

consequently lock in new advertisers on both sides of the market (Rysman, 2009). This

line of research also highlights the potential of value extraction when positive network

externalities exist on both sides of the platforms. Accordingly, the key factor underlying

the growth of such digital services is based on the logic of growing positive network

externalities either directly (on the same side of the platform) or indirectly (on the other

side of the platform).

22 Any open application offering social media capabilities can thus be considered as

a multi-sided market (aka platform involving multiple market based and social exchange

processes). When viewed as a market based process, the platform is composed of a user-

advertiser dyad where actors proffer promotional material based on economic

transactions and where offered, information or advertising services contain inherent

value. Here the created markets are used as a means to determine and exchange the value

e.g. through auctioning or other means. When a social media platform is viewed through

this lens, the user-advertiser dyad is placed on the foreground and related demand and

supply factors that drive economic benefits of joining the information sharing network

are emphasized.

The past research on two sided markets has focused on the role of pricing that

reflects the utility gained by each side in order to attract both sides to the exchange (see

Curien & Moreau, 2007; Evans, 2003; Evans & London, 2008; Evans & Schmalensee,

2005; Palmatier et al., 2006; Rochet & Tirole, 2003, 2004). For example Eisenmann et al.

(2006: 1) argue the key challenge for two-sided markets is to “get pricing right”, i.e.

‘subsidize’ one user group while charging the other a premium for access to the first. Due to indirect network externalities the favored pricing strategies are often based on a

“divide-and-conquer” tactics, where one side of the market is heavily subsidized by fees paid by the other. This allows for a wide array of flexible pricing strategies such as flat fees, subscription fees, advertising fees, and transaction value based fees. For example,

Rochet and Tirole (2003) estimate the influences on the allocation of the fees between the

two sides of a credit card platform based on such things as platform governance,

differentiation, and compatibility as well as volume-based pricing and externalities.

23 Caillaud and Jullien (2003) analyze imperfect price competition between service providers such as dating agencies, real estate agents, and internet websites by examining

the impact of informational intermediation of the platform provider via e.g. certification,

advertising, and mechanisms price discovery. According to Armstrong (2006), the

agent’s (j) gross utility (u) from using platform (i) is:

Here, is the number of agents present on the other side of the market and is the agent benefit from interacting with them. The utility is the benefit of utilizing the

platform. Most studies assume the platform benefit ( ) is not dependent on the agent or the platform and therefore can be discarded while the agent benefit is dependent on both.

In contrast, Armstrong’s assumption is that the platform benefit is dependent on the user and platform, but the agent benefit is not dependent on either. For the user-advertiser dyad on SNSs, this economic model makes sense in that the gross utility is not only dependent on the number of users and the benefit associated with them – in online advertising this is often the click-through rate or conversion rate for product sales – but is also dependent on the benefit of the platform to these transactions. This platform benefit is often determined by how well the SNS can target users based on profiles or behaviors while online.

Though insightful and helpful in analyzing the user-advertiser dyad, where the economic value of the service offered through the platform is a function of the number of users, these models fail to explain the user-user dyad exchanges of UGC. When factors other than price stimulate the growth of a platform, such as when users co-creating value

24 through UGC or co-produce the features, these economic based models fall down. The main reason for this failure is that, these models do not account for situations where most or all services are free on one side of the market. Such is the case on SNS where the offered service does not relate to economic activity and thus relies on extensive co- creation of content through UGC by the user-user dyad and co-production of the platform through the platform-developer dyad (i.e. the users are the developers). In these cases content is generated and consumed for free and the platform is co-produced by the users for free.

THEORETICAL FRAMING

Previous research has attempted to explain platform and system growth through a variety of lens including: an economic lens of two-sided markets (Armstrong, 2006), network externalities (Caillaud & Jullien, 2003), and membership externalities (Rochet &

Tirole, 2006); through a technological lens of technology adoption (Davis, 1989;

Venkatesh & Davis, 2000) and diffusion of innovation (Rogers, 1962); a sociology lens of social capital (Ellison et al., 2007); and even with a marketing lens through word-of- mouth (Ferguson, 2008) and viral marketing (Watts & Peretti, 2007). However, none of these directly and exhaustively explain the antecedents to viral growth of SNS. This remains an understudied and increasingly important area of research for both the theoretical and practitioner motivations to understand it more clearly.

In order to more fully explain the viral growth of SNS and in particular the motivations of users to participate in generating and consuming UGC as an antecedent to fanning out or recommending the service to others and continuing to use the service themselves, I turn to SET that heretofore has not been associated with explaining

25 platform growth. A review of the prior research in SET as to what it informs and what is left unanswered follows.

The ‘two-sided market’ for the voyeur and exhibitionist is structured to allow the flow of UGC in the form of status updates, pictures, links, and comments. In exchange for the content provided by the exhibitionist, voyeurs provide “eyeballs” and occasionally feedback and new types of “meta-content” to boost the ego of the exhibitionist. As

Cropanzano and Mitchell (2005: 876) note, the process of social exchange (through

UGC) ‘‘begins when at least one participant makes a ‘move,’ and if the other reciprocates, new rounds of exchange initiate.’’ This is somewhat analogous to the market dynamics of online advertisers, who receive “eyeballs” and occasionally clicks or conversions in exchange for the content that they support through advertising fees.

To address the phenomenon of the motivation of users to exchange content without monetary incentives, I adopt SET to explain how value in these dyads is created.

SET has been advanced significantly over the past five decades since the early writings of

Homans (1958), Blau (1964), and Thibaut and Kelley (1959) with its explanatory value having been explored in multitude of disciplines. The results include applications as diverse as social power (Molm et al., 2000), organizational networks (Brass et al., 2004), board independence (Westphal & Zajac, 1997), citizenship behavior (Konovsky & Pugh,

1994), psychological contracts (Rousseau, 1995), and leadership (Liden et al., 1997).

There are two fundamental concepts within SET that have been posited to explain the underlying motivations – rules and economic value. The observation of certain

“rules” of exchange is a basic tenet of SET that generates trust and loyalty within relationships. Rules of exchange define a “normative definition of the situation that forms

26 among or is adopted by the participants in an exchange relation” (Emerson, 1976: 351).

One of the most fundamental rules is the expectation of reciprocity. From classic anthropology studies (Malinowski, 1921, 1922), it was deemed appropriate to view social exchanges in terms of economic value. However, exchanges were also shown to have symbolic relevance – standing for something beyond material properties (e.g., approval and prestige). Both of these ideas are still a part of SET and are embraced by contemporary scholars in this area (Cropanzano & Mitchell, 2005).

Homans (1958) writings bridged a variety of disciplines, sparking differing theories of social exchange, and while theorists diverge on particulars, they do converge on the “essence” of SET – social exchanges are comprised of actions that are contingent on the rewarding reactions of others, resulting in mutually rewarding relationships

(Cropanzano & Mitchell, 2005). SET assumes self-interested actors who transact with other similarly self-interested actors to accomplish goals that cannot be achieved alone and as Lawler and Thye (1999: 217) state “self-interest and interdependence are central properties of social exchange.”

The SET model suggests that “people are motivated by the desire to gain resources in exchanges with others” with a secondary motivation of interacting with others being the use of information to define their social identity (Tyler, 2001: 301).

According to the Theory of Information Sharing, which has its roots in SET (Chan,

Bhandar, Oh, & Chan, 2004), sharing information as an expert contributes towards the formation of a person’s self-identity and arises from the need for self-expression

(Constant, Kiesler, & Sproull, 1994). Through this theoretical framework I find the

27 exchange of information as a basis for the formation of one’s self-identity. I will use SET

to inform my theoretical model of individual motivators of self-identity.

What Is Known

It is well accepted in literature that there are three critical factors that influence viral growth – fan out, conversion, and retention. Fan out and retention represent behaviors of an engaged or converted user. Conversion, in contrast, is a behavior exhibited by a potential user. These factors are well established as the calculable variables to predict and explain viral growth of platforms.

From the literature of two-sided markets, it is known that the economic models predictably explain the growth of platforms where the economic value of the service offered through the platform is a function of the number of users. When the gross utility of using the platform is not only dependent on the number of users, but also on the benefit associated with them and the benefit of the platform to these transactions, these models make sense. Researchers also know that these models do not make sense when factors other than price stimulate the platform growth, such as when users co-creating value through UGC or co-produce the features. These models fail to explain the user-user dyad exchanges of UGC, because they do not account for situations where most or all services are free on one side of the market, such as on SNS.

It is known that individual level models of explaining acceptance and use of computing technologies have been employed successfully for decades. One of the most widely utilized of these, TAM, has been used to explain adoption of e-commerce (Gefen

& Straub, 2000), Internet usage (Moon & Kim, 2001), personal computer operating systems (Karahanna et al., 1999), and many other single user, single tool technologies.

28 Because SNS platforms are fully technology-driven and are a new type of technology that each individual user must adopt, it makes sense that I incorporate the antecedents of the nomological structure of TAM – perceived usefulness and ease of use – as predictor of sustained viral growth of SNS.

Researchers do know that certain technological aspects such as page download time and the user-interface have been demonstrated to affect usage of search (Brutlag,

2009) as well as purchases on e-commerce sites (Lohse & Spiller, 1998). However, it is also true that SNS platforms are highly evolving, multi-technology, open IT systems that are designed to mimic the very complex social interactions of individuals and therefore not likely to be explained completely through single user/single technology adoption theories. It is unclear how the antecedents of TAM or other technology aspects affect the viral growth of SNS platforms. Next I will review what is not known with regards to this growth.

What Is Not Known

Despite researchers’ understanding of the fact that mediated exhibitionism and voyeurism relies on and even spawns the creation and consumption of UGC, it is not known what underlying causal mechanisms facilitate this participation. Why do some platforms continue to have users freely generate and consume UGC, while others struggle to either get started or sustain? As it is understood that the growth in UGC is achieved through affecting the scope and intensity of social exchanges amongst the users, why does this vary on certain SNS?

While TAM has been used in a myriad of applications, it is not known how much explanatory power TAM has for the factors of viral growth of SNS platforms. Can TAM

29 also be used to explain very complex, multi-user, multi-tool technologies such as the modern platforms? It is not known if all, some, or none of the constructs in the nomological model of TAM can be used to further explain fan out or retention.

Researchers do not know how technological features such as page load time or user-interfaces affect the viral growth factors of SNS. It is understood that a compendium of research has indicated that these factors are applicable to a wide range of technologies, but this does not provide quantifiable evidence of the affect that they may or may not have on users’ participation in fan out or their continued usage of the platform.

The SET model suggests that people are motivated to participate in exchanges with others in order to gain information about the others self-identity and to expose their own social identity. However, how these motivations at the individual or social level affect the continuous UGC and viral growth factors is not known. The fundamental concept within SET of following rules has been used to explain how users generate trust and loyalty through participation in social exchanges. It is left unknown how rules such as “reciprocity” are used or not within the social exchanges on SNS and if so how they affect viral growth.

These three areas – the underlying causal mechanisms that facilitate UGC, the amount of explanatory power in TAM, and the exogenous technological, individual, or social factors – are left unknown with regards to how they affect the sustained viral growth of SNS. These unknowns are the foundation of my research questions. In Table 1,

I map these unknowns and research questions as well as the methodology that is required in order to answer them.

30 TABLE 1: Unknowns Mapped to Research Questions

Unknown Research Question Methodology Justification Causal mechanisms What underlying causal mechanisms, Quantitative - required for theory that support or thwart in terms of how users exchange UGC generation with regard to the UGC and innovate on the platform, possible relationships between produce viral growth among SNS? service attributes and individuals’ attitudes or behaviors that increased viral growth.

Amount of To what extent do individually based Qualitative – required to reveal the explanatory power of attitudes explain viral growth versus range of personal level factors and TAM for the viral factors that drive social exchanges behaviors that influence fan out growth of SNS related to co-creation and co- and retention on a SNS. production on a SNS?

Technological, What possible technological, Qualitative – required to reveal individual, or social individual, or social antecedents will additional exogenous factors constructs in the drive these processes? impacting co-creation and co- model production on a SNS.

Next, I will explain in more detail and further justify the methodologies that I undertook in order to answer these research questions.

RESEARCH METHODOLOGY

Research Design

Mixed methods research has been called the third path (Gorard & Taylor, 2004) and has emerged as an alternative to the dichotomy of qualitative and quantitative approaches (Tashakkori & Teddlie, 2003). From 19 different definitions found in literature, Johnson, Onwuegbuzie, and Turner (2007: 123) define mixed methods research as:

Mixed methods research is the type of research in which a researcher or team of researchers combines elements of qualitative and quantitative research approaches (e.g., use of qualitative and quantitative viewpoints, data collection, analysis, inference techniques) for the broad purposes of breadth and depth of understanding and corroboration.

31 Based on this definition, I intend to pursue a mixed methods approach utilizing both

quantitative and qualitative research strategies. This is driven by my motivation of

achieving a more accurate and valid theory to understand the viral growth phenomenon.

There are at least five purposes for mixed method approaches – triangulation,

completeness, development, initiation, and expansion (Greene, Caracelli, & Graham,

1989; Tashakkori & Teddlie, 2003). In this context, development and triangulation are

my two motivations for a mixed method design.

Development – broadly construed to include sampling and measurement decisions

– seeks to use the results from one method to develop or inform the other method

(Madey, 1982) and is my motivation for utilizing qualitative methods to inform my quantitative track. Triangulation refers to “the designed use of multiple methods, with offsetting or counteracting biases, in investigations of the same phenomenon in order to strengthen the validity of inquiry results” (Greene et al., 1989: 256) and is my motivation for ensure I generate the strongest theory possible by utilizing two approaches with differing strengths as to acquire alternative results (see also Denzin, 1978; Mathison,

1988). Thus, the core premise of triangulation is that all methods have inherent biases and limitations; therefore, two or more methods that have offsetting biases provide findings that are enhanced.

It has been argued that mixed method research does not align itself with a single system or philosophy (Creswell, Plano Clark, Gutmann, & Hanson, 2003), because of its pragmatic approach (Harrison & Reilly, 2011). Instead it is most often driven by a pragmatic, problem driven research question, rather than being restrained by paradigmatic assumptions (Johnson & Onwuegbuzie, 2004). In my case I have such a

32 research question as outlined above in Table 1. I also believe that my research problem is ideally suited for a mixed methodology study as it studies an emerging and new phenomenon in a context. Therefore, I need to use qualitative methods to sensitize to the influence of the context and to observe specific behaviors in that context before embarking on to a quantitative study. Finally, my study involves interactions between individuals’ attitudes and behaviors within a socially complex environment consisting of

SNS platforms that facilitate these interactions. In this sense my study context is multilevel study which is better suited to be examined using a mixed method approach.

However, the analysis of the direct effects of individual and social constructs on the dependent variables of fan out and retention on a SNS is ideally suited for a quantitative analysis. This will aid me in identifying which factors and in what amounts do they offer explanatory power of viral growth.

My research is divided into three studies as diagramed in Figure 2. These three studies begin by addressing the differences between two SNS platforms with different levels of viral growth and associated individual user behaviors. Because the lack of a theory to adequately explain the phenomenon of sustained viral growth of SNS from both the user and platform perspectives, I engage first in theory development using a qualitative, grounded theory approach. Informed by insights from this research, I then compare and contrast the differences between the social exchange model and the classic technology acceptance model in explaining the viral growth utilizing a quantitative approach. Utilizing the quantitative results combined with more detailed explanations of growth promoting user behaviors derived from the qualitative study I thereby formulate a deeper set of research questions to exploring the underlying mechanisms for user

33 interactions on SNS. My third study dives deeper into the ratios of user behaviors as demonstrated within users and between users which is informed by the results of the qualitative study. Combined these three studies seek to formulate and validate a theory of viral growth of digital platforms in one theory generation and validation cycle.

FIGURE 2: Overall Research Design

I utilized a sequential (QualQuant according to Morse’s (1991) Notation) development and triangulation model of mixed methods where one qualitative study is conducted prior to the quantitative study, but where through several revisions and cross- referencing of data and results the studies are used to strengthen and inform each other

(Figure 3). To this end I begin with the qualitative strand by conducting semi-structured phenomenological interviews with users and executives of SNS platforms. The product from this – codes, themes, and theory – informs the quantitative theoretical model

34 development. Additionally, the results of the quantitative study are cross-referenced with

the qualitative results to further explain the results in the final integration. This triangulation of results and theory utilizes the qualitative observations to guide the quantitative study and then help clarify the quantitative results allowing for much deeper and contextually valid insights (Creswell et al., 2003; Tashakkori & Teddlie, 2008). In

this manner, I benefit from mixing methods sequentially in which each method –

qualitative and quantitative – has equal standing.

FIGURE 3: Triangulation Model

In the remainder of this section, I will revisit research questions associated with

qualitative and quantitative strands and identify specifically what methods and techniques

35 were deployed to answer them. As noted, the two key research questions guiding the

inquiry were:

1. What underlying causal mechanisms, in terms of how users exchange UGC and innovate on the platform, produce viral growth among SNS?

2. To what extent do individually based attitudes explain viral growth versus factors that drive social exchanges related to co-creation and co-production on a SNS? What other possible technological, individual, or social antecedents will drive these processes?

The first research question is broad and unstructured where I attempt to identify poorly understood individual, social, and technological factors underlying viral growth.

This question investigates, in particular, the interactions between individuals’ attitudes and behaviors on SNS platforms as well as associated organizational processes that enable the SNS platform to modify and adapt its components to service the need of the individuals and promote growth. As I observe, many of these factors are personal and evoke strong emotional reactions that have not been captured by prior models of technology adoption and use. To address this question I therefore turn to a qualitative approach that resulted in the first paper, “The Co-Production of Viral Growth”, which is summarized in the overview section and attached as Appendix B.

The second research question is more detailed and specific, and seeks to develop and refine the insights obtained from the first study. In particular, I attempt to discern the level of explanatory power TAM, unique identity related factors such as exhibitionism, technological factors, and social exchange processes have in explaining viral growth in

SNS. To answer these questions and further investigate the viral growth phenomenon I turn to use quantitative survey based research methods. The selected set of constructs – such as specific individual level exhibitionism or participation in the co-production – are

36 now made operational and measured through the use of multi-item scales. This research results in my second paper, “The Antecedents of Viral Growth on Social Networking

Platforms”, that is summarized in the overview section and attached as Appendix C. In order to explore in greater detail the effects of individual and social exchange factors as revealed by the second study I next test the effects of proportions of individual motivators such as exhibitionism and voyeurism present in the use of SNS and influencing viral growth. This requires the use of additional quantitative methods such as cluster analysis and advanced structural equation modeling techniques such as multi-group analysis. This research results in my third paper, “The Influence of User Mix on the Viral Growth of

Social Networking Site”, that is summarized in the overview section and attached as

Appendix D.

In summary, I argue that the deployed mixed method approach offers the best opportunity for both a broad and deep understanding of the viral growth on SNS in answering my two research questions. I made use of a special type of mixed method design that involves sequential development and triangulation of results and data. Next, I will cover the research strands that I used to answer the research questions. The qualitative approach will follow mainly a grounded theory approach as suggested by

Strauss and Corbin (1990) and the quantitative approach will include use of a survey method using structural equation modeling and clustering techniques.

Qualitative Strand

37 between product attributes and individuals’ attitudes or behaviors that increased viral growth – fan out, conversion, and retention rates – of SNS. Phenomenological, semi- structured interviews, informed by grounded theory principles of (Strauss & Corbin,

1990), were conducted with customers of Facebook and Friendster. Semi-structured interviews allowed for structure and uniformity in the collection of data, but preserved flexibility and opportunity for the emergence of novel contributions from respondents. I interviewed both users as well as executives of two SNS in order to obtain a robust set of data to analyze.

The data was interpreted using analytical methods recommended by Strauss and

Corbin (1990) including constant comparison and theoretical sampling. Emergent themes and concepts directed forward sampling which continued until no more themes or concepts could be identified, signaling theoretical saturation. From this analysis I developed a conceptual model of viral growth that became the basis of my hypothesized model that was tested via the quantitative methods.

Quantitative Strand

To empirically test the proposed model, I surveyed users of five of the 2010 top social networking sites – Facebook, Twitter, LinkedIn, MySpace, and Ning1 – and three social networks that had failed to achieve sustained viral growth in the United States (e.g.

Friendster, Yahoo! 360, and Orkut). To this end, I followed a psychometric survey methodology that maps individual responses to the underlying constructs within my model. My model involved 11 constructs all of which were measured with reflective scales.

1 According to http://www.ebizmba.com/articles/social-networking-websites 38 I used a “snowballing” technique to reach a diverse sample of SNS users. Two primary approaches were used to collect data. First, I leveraged the personal and professional network of the researchers by posting the link to the survey on the social networks being studied asking for participation and for assistance distributing the request by reposting to their networks. This “snowballing” technique is amenable to the same scientific sampling procedures as ordinary sample (Coleman, 1958). Using this method I received 432 responses. Second, I distributed the survey via email to 229 undergraduate and 618 graduate students at the Weatherhead School of Management. I received 343 completed responses. To maximize response rates, I guaranteed anonymity, collected no personally identifiable information, and assured respondents that only the researchers would have access to the raw data. In total, I received 775 respondents, with a 14.1% dropout rate, categorized as such if more than 10% of the responses were missing. The remaining 666 respondents provided 1449 cases for analysis as respondents answered for multiple social networks.

Structural equation modeling (SEM) provides the ability to simultaneously estimate multiple dependent relationships and incorporate multiple items for each of the concepts (Hair, Anderson, Tatham, & Black, 2010). This study followed a two-step SEM approach as recommended by Anderson and Gerbing (1988) where a factor analysis model specifies the relations of the observed measures to their posited underlying constructs and a confirmatory structural model then specifies the causal relations of the constructs to one another.

39 OVERVIEW OF MAIN RESULTS

Up to this point in the dissertation I have used singular pronouns – I, me, my – to reflect the single ownership of this work. However, as the following papers were written by a team of researchers, I will switch to the use of plural pronouns – we, us, our – to indicate the collaborative nature of this work and ensure credit is shared. I will switch back to the use of first person pronouns in the conclusion section to once again indicate the individual authorship of the paper.

Paper 1: The Co-Production of Viral Growth: A Comparative Analysis of Two

Social Networking Sites

The following is a summary of our first paper that resulted from attempting to answer the first research question. The full paper minus the literature review can be found in Appendix B. In this study, we sought to address our first research question – What underlying causal mechanisms, in terms of how users exchange UGC and innovate on the platform, produce viral growth among SNS? I attempted to discern and examine possible relationships between individual, social, and technological factors such as individuals’ attitudes and behaviors that increased the viral growth of SNS through fan out, conversion, and retention. I also sought out organizational processes that enable the SNS

platform to modify and adapt its components to service the need of the individuals. To

this end, phenomenological, semi-structured interviews, informed by grounded theory

principles of (Strauss & Corbin, 1990), were conducted with customers and executives of

Facebook and Friendster to engage in a theory generating grounded study. Our sample

consisted of 29 SNS users − 19 Facebook subscribers and 10 members of Friendster. A

second group of respondents included two former executives of Friendster, two

40 current/former executives of Facebook, and one consultant who worked with both

Facebook and Friendster.

We identified that Facebook and Friendster had significantly different approaches to product development which led to very dissimilar user experiences. The expression of user’s self-identity was supported on Facebook, as demonstrated by the executive who stated “we see one of our core use cases [as being] identity and identity management.”

Facebook users confirmed that they constructed their self-identity through behaviors including the development and maintenance of relationships, sharing, controlling diverse worlds, reputation management, and living vicariously.

Paper 2: The Antecedents of Viral Growth on Social Networking Platforms

In this study we attempted to answer our second research question - To what extent do individually based attitudes explain viral growth versus factors that drive social exchanges related to co-creation and co-production on a SNS? The full paper minus the literature review can be found in Appendix C. Therefore, we developed a hypothesized model that was informed and guided by the conceptual model developed from the qualitative study. We first wanted to know how much explanatory power TAM, a single user-tool adoption theory, had on viral growth due to the fact that SNS platforms as such are considered a type of innovative technology for user to adopt. The antecedents of adoption in TAM – perceived usefulness and ease of use- were posited to predict the user behaviors of fan out and retention – not just their intention to adopt. We next desired to determine to what extent individually based psychological motivators- voyeurism and exhibitionism- expanded from the self-identity factors found in the qualitative study, explained viral growth. Additionally, we wanted to investigate how participation in co-

41 creation and co-production mediates or directly influences viral growth. To this end, we developed a set of hypotheses and constructed three hypothesized model including a mediated model.

The first model was a modified TAM model where antecedents of page load time, user interface, and privacy policy influenced perceived usefulness and perceived ease of use which influence fan out and retention. The second model was the Social Exchange model that hypothesized that the processes of co-creation and co-production will mediate the effect of voyeurism and exhibitionism on fan out and retention. The third model was the “blended” or combined model that integrated the TAM and Social Exchange models.

The TAM model resulted in very good explanatory power of 42% and 56% and the overall predictive power was similar to the results associated with TAM3 (Venkatesh and Bala (2008). Our combined model demonstrated greater explanatory power- 56% and

65% explained variance for fan out and retention- demonstrating a 16% and 33% improvement over the modified TAM model alone. Calculating the squared partial correlations as recommended by Cohen (Cohen, 2003) we measured f2 = 0.24 for fan out and f2 = 0.20 for retention, both representing between medium and large differences in the R2 of the models. The final structural model is shown in Figure 4 below.

42 FIGURE 4: Structural Model

Perceived ease of use. Our results indicated that perceived ease of use was not a

significant predictor of fan out behavior.

Page load time. We found that page load time was a significant direct predictor of

fan out (β = 0.16, p< 0.001) and retention (β = 0.26, p < 0.001).

Co-production. The level of co-production did not affect retention but did affect fan out. We did find support for a chained mediation from exhibitionism through co- creation and co-production to retention. Our finding suggests that co-creation is a significant antecedent to co-production (in contrast to service dominant logic models

(Hilton & Hughes, 2008; Lusch & Vargo, 2006a)).

43 Self-identity. Voyeurism had a negative effect on fan out and retention indirectly through co-creation. In contrast, exhibitionism was found to have a positive direct effect on both retention and fan out; and it partially mediated positively through co-creation and co-production, in the combined model. Our findings support thus earlier research and build upon it by demonstrating the distal mediation of exhibitionism on retention and fan out is chained through both co-creation and co-production (Calvert, 2004). Users who are mostly exhibitionists drive fan out and are critical for viral growth, as they want others to see what they do.

Paper 3: The Influence of User Mix on the Viral Growth of Social Networking Sites

In this study we desired to further understand our second research question - What other possible technological, individual, or social antecedents will drive the co-creation and co-production processes? The full paper minus the literature review can be found in

Appendix D. Guided by the conceptual model developed from the first qualitative study we hypothesized and tested a more refined research model that extended the findings from the first quantitative study. We also utilized the results of our qualitative study to assist in explaining our findings. Our objective was specifically to explore how the individual and the network ratios of voyeurism to exhibitionism affect viral growth on the

SNS platforms. Here we investigated in particular the moderating and interaction effects of the individual SNS platform as well as the voyeurism/exhibitionism ratio. We used the same data set as in the first study. We utilized structural equation modeling (SEM) for data analysis to study the causality between model elements. We used the clustering method to identify groups of users within and between users on a SNS with different voyeurism and exhibitionism profiles to test our hypotheses.

44 Our results revealed that the level of voyeuristic and exhibitionistic behavior varies depending on which SNS the users are participating in and thus is extrinsic. Thus,

SNS should strive to facilitate users to have the ratio of voyeurism and exhibitionism that most promotes fan out and retention. Thus, the next logical question is what amount of voyeurism and exhibitionism is most likely to produce sustained viral growth? Not surprising, we found that unengaged users had little effect on fan out and retention, whereas users with a ratio of high voyeurism and medium exhibitionism had higher effect on retention. We did identify that regardless of the level of exhibitionism, the level of voyeurism predicted the fan out – low voyeurism equated to low fan out while high

voyeurism equated to high fan out. Thus the SNS attempting to achieve sustained viral

growth should attempt to produce a platform that facilitates high levels of voyeurism and

a moderate (medium) amount of exhibitionism. This combination appears to be the most

likely to produce high levels of fan out and sustained retention.

Summary of Findings

In summary, we have found through our three studies that factors involving the

facilitation of co-creation of UGC and individual behaviors involving the creation and

management of one’s self-identity were drivers of viral growth of SNS. We have further identified that the combination of these individual behaviors and platform processes – which we refer to as the social exchange model – are better predictors of viral growth than the TAM alone. Specifically, voyeurism, a behavior associated with self-identity, has a negative effect on fan out and retention when mediated through co-creation whereas exhibitionism, also associated with self-identity, has a positive direct effect on both retention and fan out, partially mediated through co-creation and co-production. Our

45 findings further suggest that the level of voyeuristic and exhibitionistic behavior varies

depending on which SNS the users are participating in and thus is extrinsic. Further, it

was demonstrated that when users are “matched” with the SNS in terms of having similar

voyeuristic-to-exhibitionistic ratios there was greater Fan Out and Retention. We found

that, as expected, unengaged users had little effect on Fan Out and Retention, whereas

users with a ratio of high voyeurism and medium exhibitionism had higher effect on

Retention. And lastly, we identified that regardless of the level of exhibitionism, the level

of voyeurism predicted the Fan Out – low voyeurism equated to low fan out while high

voyeurism equated to high fan out. Next I will discuss our conclusions.

CONCLUSIONS

Discussion

In my quest for a theory to explain the viral growth of SNS platforms, I

established two research questions to guide me. The first question drove me to investigate

the underlying causal mechanisms of UGC exchange that affect viral growth. In answer

to this, I have revealed the pivotal role of creating and consuming self-identity through

the behaviors of voyeurism and exhibitionism. In some regard this should not come as a

surprise, since over the past two decades in an array of social sciences (Ashmore &

Jussim, 1997) a resurgence of interest in self and identity has arisen (Whittaker, 1992).

The modern concept of self-identity, especially as was observed to be displayed through

SNS, originates from McCall’s and Simmon’s (1978) and Stryker’s (1980, 1987) ground breaking work on multiple-role of identity. Previous research in self-identity has been shown to predict behavior (Sparks & Shepherd, 1992; Terry, Hogg, & White, 1999).

Markus and Nurius (1986) argue that the possible selves are different and separable from

46 the current selves and are not just imagined roles, but rather specific representations of an

individual’s hopes, fears, and fantasies, which all able to be initiated and refined through

SNS.

My second research question drove me to investigate how individual attitudes that

drive the exchange of UGC are related to co-creation and co-production. In answer to this, I discovered that in order to support the creation and consumption of self-identity, the SNS needed to facilitate the co-creation of value through UGC and encourage customers to co-produce the SNS by misusing features and functionality. My findings also suggest that co-creation is a significant antecedent to co-production in social networking platforms. This makes sense in my study’s context in that the co-creation of new content through the ‘misuse’ of services is the means by which co-production on digital platforms is most often carried out.

The remainder of this section is organized as follows. I will start by discussing the conclusions that were revealed from the investigation of my first research question: 1) self-identity and the social behaviors of voyeurism and exhibitionism; 2) the reciprocity of social exchanges and the impact on UGC; 3) the differences in platform tactics of evolution. Next, I will discuss the topics that were revealed while answering my second research question: 1) the overall social exchange model as compared to traditional technology acceptance models and 2) extrinsic nature of the ratio of voyeurism and exhibitionism for users.

Understanding self-identity and social behaviors. At the foundation, my study found that the growth of SNS is motivated by the creation and management of users’ self- identities, where the ‘self’ emerges through interacting with others (Prus, 1997). The

47 UGC on the sites serves to promote social exchanges, which either reveals facets oneself

to others (exhibitionism of one’s self), or allows one to see others in new ways

(voyeurism of other’s selves). As a result, the nature of UGC on social network sites is

vastly different from the producer centered content in traditional media (Jones, 2010). In

short, digital capabilities of SNS enable constant recording and presentation of an

individual’s daily experience and expression of different potential selves (Koskela, 2002).

Thus, the motivations underlying a user’s site behaviors are to satisfy exhibitionistic

(Munar, 2010) and voyeuristic desires (Calvert, 2004: 2).

On SNS users become the objects of the constant gaze of others – visible all the time, watching and being watched. My study revealed that users adapt to several different roles in virtual communities, producing a multidimensional virtual identities, which can take many shapes depending on the context and means of communication. There is no single virtual identity or singular way in which social networking sites contribute to identity building. Instead it comes in many diverse shapes and is constructed through a multiplicity of interactions where users create a collage of multiple, fluid and complex expressions of self (Munar, 2010). This has come even so far that each encounter creates a new identity in virtual world. Indeed, in 2009, Facebook’s privacy controls allowed users to share varying degrees of information with friends, but Zuckerberg talked about eventually giving each user the ability to have a different Facebook personality for each

Facebook friendship (Hempel & Kowitt, 2009). Next I will discuss the differences in

SNS that facilitate the creation and display of self-identity through the processes of co- creation and co-production.

48 Reciprocity of user generated content. My study looked to SET to help explain

the user motivations for participating in generating UGC on SNS. SET has been applied

to a diverse set of situations such as primate relationships (De Waal & Luttrell, 1988),

social power (Molm et al., 2000), and organizational networks (Brass et al., 2004), and

many other areas. Thus I was interested in whether or not SET could be extended to

apply to SNS.

The observation of rules that define normative behavior during an exchange is a

basic tenet of SET (Emerson, 1976) and one of the most fundamental rules is the

expectation of reciprocity which has been well established for many decades

(Cropanzano & Mitchell, 2005). Indeed, many sociologist concur that reciprocity is of the

utmost important concepts, Howard Becker went so far as to refer to man as Homo

reciprocus, but few have attempted to define or qualify what constitutes reciprocity

(Gouldner, 1960). I investigated whether reciprocity could come in the form of

exhibitionism of one’s self-identity on SNS.

I applied the rule of reciprocity to users engaging in social exchanges to determine, if a balanced ratio of voyeurism to exhibitionism within individual users increased fan out and retention of the SNS. In fact, my study revealed that the SET rule of reciprocity is highly applicable to understand activities on a SNS, specifically within

users with regard to the balance of voyeurism and exhibitionism. Users with balanced

ratios of voyeurism and exhibitionism feel equanimity in their relationships on SNS and

are more interested in creating UGC – thus having more opportunities to participate in

co-production and misuse the product in new and creative ways.

49 I additionally applied the rule of reciprocity across the user and the SNS to explore whether a “matched” ratio of voyeurism and exhibitionism between the individual and the SNS affected viral growth. My results demonstrated that when users felt their levels of voyeurism and exhibitionism were similar to everyone else on the

SNS, they would fan out and be retained the most. When users feel most comfortable that the amount that they reveal will be reciprocated, they are more likely to suggest the SNS to others and continue using it themselves.

These findings are important as they provide the justification for the extension of

SET into SNSs. Specifically, I have supplied evidence that the user motivations of voyeurism and exhibitionism fit into the theoretical framework of SET as constructs for the reciprocity in that if one exhibits his or herself in some manner the voyeur of that exchange is compelled to exhibit something back. Sometimes this occurs near real time during the exchange through a status update and other times it occurs at a temporal distant over days and weeks as updates, pictures, and virtual links are shared on the SNS.

Thus, I have shown evidence for the presence of reciprocity within social exchanges which can arise due to exhibitionism of one’s self-identity on SNS.

Differences in platform behaviors and learning. One key differentiating process for growth was the level of co-creation of UGC. Another key process was the level of co- production (Schultze & Bhappu, 2005). Facebook actively sought the value added from co-production of their product with customers. This resulted in a customer centric social construction of the product. Friendster, in contrast, discouraged co-production, seeking no external input and actively rejecting it. This resulted in an internally established social

50 construction of the product, where the engineering team attempted to impose meaning and preferred manner of use of the product on the customer.

The result of these contrasting approaches to production and construction of services yielded dramatic differences in organizational learning within the firms and their growth performance. The resulting organizational learning process explains partially varying organization performance outcomes through the mediation of different types of organizational knowledge that took place on the platform (Kogut & Zander, 1992).

Friendster was preoccupied with solving technically challenging problems of the F-graph or friend-graph and paid little attention to user feedback; they actively sought to prevent users from using the service in any other manner than how they intended it to be used.

Friendster’s engineering centric focus resulted in an organizational learning where the discord between current and desired state was thought of in terms of technological solutions. The resolution of these challenging issues took significant amounts of time and resources, but prevented the development of and learning from customer desired features.

Facebook created a culture of pushing the limits of product feature acceptability and consistently monitored customer usage. Customers provided Facebook feedback through their use and misuse. As new features were launched, the viral growth team studiously monitored usage and shared statistics to ensure features were not inhibiting the way users desired or discovered to use the service. This concerted focus on viral growth and facilitation of co-production resulted in learning where the dissonance between desired and current state was constantly articulated in terms of customer retention and recruitment. When solutions to fill this gap were identified the Facebook team generalized the learning across other features within the platform.

51 Social exchange model. Social networking sites are more than just a set of

functional technologies supporting the tasks of a single user; rather, they are arenas that

promote social exchanges through the co-creation of UGC that allows users to express

their self-identity. Thus, SNS need to be understood as an evolving service ecology that is

jointly created by users and service providers (Jung & Lyytinen, 2009). Yet, due to the

individualistic and cognitive focus of traditional technology acceptance models, such as

TAM, the drivers and determinants of social exchanges and features of platform

characteristics leading to retention and fan out have not been explored. I built and tested a

model that draws upon concepts of social exchange and thus integrates the behaviors that

support the creation and management of self-identity – voyeurism and exhibitionism –

with the processes of generating UGC on the SNS – co-creation and co-production. I also test this model against TAM to determine which had the most explanatory power for viral growth.

I found support for a chained mediation path from exhibitionism through co- creation and co-production to retention. This makes sense because in order to co-produce

a product one must use it first in the form of co-creation of UGC. Users also co-create

content on SNS as they create and consume each other’s self-identities. As one user

initiates the process by posting something – perhaps the name of a trendy restaurant

where are currently eating – this expresses to others of the sender’s self-identity. Should

these consumers respond – perhaps stating how much they enjoyed their last meal there

on a platform – they too are expressing a statement that will reflect on their self-identity to all others who not only read the first post but the following comments in the chain.

One particularly creative user might have the idea to post how many times they have

52 been to that restaurant. Ever watchful developers – both internal to the SNS and external third-parties – might pick up on this and develop a feature or add-in that tracks visits and appoints people “mayor” of a location, if they have visited it more than anyone else.2

Thus, users participating in co-creation can inadvertently or purposefully participate in co-production of the SNS platform.

Additionally, I found that the combined model had better explanatory power than the TAM model alone. These findings suggest that individual adoption factors alone do not adequately explain the growth of or the continued use of the social networking platforms. While technology factors and individual factors remain important and necessary components, there is more to the story when dealing with SNS and the complex social interactions that take place on those platforms. Especially for continued growth the social exchange is important. Recent research has demonstrated that self- identity has direct effects on technology acceptance in voluntary situations (Lee, Lee, &

Lee, 2006) while others have concluded that, if the perceived usefulness has been met then the motivation for expression of one’s self-identity became important in the adoption of mobile services (Nysveen & Pedersen, 2003). My results demonstrate that the importance of being able to express one’s self-identity and participate in the generation and consumption of content becomes over time vital to the fan out and retention of users.

Intrinsic vs. extrinsic. My results revealed that the level of voyeuristic and exhibitionistic behavior varies depending on which SNS the users are participating in and thus is extrinsic. While insightful for the application of this to SNS, this finding isn’t surprising. I understand from much extant research on SET that the observation of rules

2 See FourSquare, http://en.wikipedia.org/wiki/Foursquare#Mayorship and the integration with Facebook, http://mashable.com/2012/05/15/foursquare-facebook-timeline-map/ 53 that define normative behavior during an exchange is a basic tenet of SET (Emerson,

1976). One of the most fundamental rules is the expectation of reciprocity which has

been well established for many decades and is still embraced by contemporary scholars

(Cropanzano & Mitchell, 2005). When users feel most comfortable that the amount that

they reveal will be reciprocated, they are more likely to suggest the SNS to others and

continue using it themselves.

Implications for Practitioners

Conventional wisdom suggests that achieving viral growth is apparently pretty straightforward and involves building the right product. For instance, one 2011 article

“Understanding Viral Growth [Marketing Math]” from an internet marketing blog

HubSpot.com states that the key ingredient among others is to “create an application with value that is so compelling, your customers will be happy to share it with others!”3

Another post, on the entrepreneur blog ForEntrepreneurs.com, states that viral growth is not a marketing strategy but rather that “It has to be built into your product right from the beginning.”4 I started this study with a similar view that the most important factor

influencing viral growth of SNS was most likely some compelling single feature. I was

pleasantly surprised by findings that this was not the case. It was more important to watch

how customers misused the product and then iterate quickly towards that product feature

set that was most important. This is much more than A/B testing5, which is commonplace

in usability designs (Barnum & Dragga, 2001), and requires an openness to accept that

3 See http://blog.hubspot.com/blog/tabid/6307/bid/9249/Understanding-Viral-Growth-Marketing- Math.aspx

4 See http://www.forentrepreneurs.com/lessons-learnt-viral-marketing/

5 See http://en.wikipedia.org/wiki/A/B_testing 54 customers will try to use the product in completely unexpected ways. In this section I will

cover two of the most important contributions to practitioners that this research provides

1) the importance of misuse in driving product development and 2) the motivation of

users to use and thus possibly misuse the product – self-identity.

Misuse or bricolage. This research provides practitioners with guidance on the

importance of facilitating co-production by allowing customers to misuse or bricolage the product. Bricolage is a term borrowed from the French verb ‘bricoler’ meaning "fiddle, tinker" and used to refer to the creation of something from diverse items that happen to be available. This is somewhat of a radical concept in that it suggests that the traditional method of product development be discarded in favor of allowing the customers to use the product in ways that it was not intended. A more traditional approach is to have engineers or product managers decide on how the product should function and then restrict the usage to this functionality. This approach, to the extreme, was what I found through my interviews of Friendster executives. What was revealed through my qualitative and quantitative studies was the importance of abandoning this approach.

Instead, the more a SNS allowed users to misuse or bricolage the product the more users suggested others join and the more they wanted to continue using that SNS.

The practitioner seeking sustained viral growth should pay attention to the way

users are attempting to use the product. Often the expectation of how users should use a

product results in tunnel vision with regards to the usage metrics that are monitored. An

alternative approach is to scan the usage logs of the platform looking for the unexpected

usage of how users are attempting to engage in product. The most important insights into

55 what functionality should be created next lie in those user behaviors that would be construed as misusing the product.

In addition to the importance of allowing users to misuse the product, this research provided insight into the motivations of why users participate in the generation and consumption of UGC and thus have opportunities to misuse the product. This topic will be covered next.

Motivations of users. While the most important process for the SNS to facilitate is co-production, the practitioner must understand what motivates the user to engage in co-production in the first place. If the users do not co-create UGC, recommend the service to others, and continue using it themselves, there is no opportunity for misuse.

The motivations of users engaged in co-creating UGC revolve around the creation, management, and projection of one’s self-identity. Specifically, the behaviors of voyeurism and exhibitionism were found to be critical to establishing one’s self-identity through the co-creation of UGC. My research also revealed insights into the importance of the ratio of voyeurism and exhibitionism both within users and between users of a

SNS.

With regards to these behaviors I demonstrated to practitioners that to achieve sustained viral growth they should attempt to produce a platform that facilitates high levels of voyeurism and a moderate (medium) amount of exhibitionism. This combination appears to be the most likely to produce high levels of fan out and sustained retention. Additionally, I provide practitioners with the understanding that users who

“matched” the SNS in terms of the ratio of voyeurism and exhibitionism were the ones most likely to fan out and continue using the service. High levels of fan out and sustained

56 retention are critical for the opportunity for users to misuse the product and create a

virtuous loop of usage, misusage, fan out, and continued usage, that creates sustained

viral growth.

All of these insights revealed by my study are of vital importance for

practitioners. As I pointed out, the conventional wisdom of product development tells

practitioners that achieving viral growth involves building the right product. Many of the

most popular guides, such as The Lean Startup, direct practitioners to set success criteria

– which necessitate the preconceived notion of how users will use the product – and then

iterate on the product until they achieve these criteria. My primary contribution to

practitioner knowledge is that this traditional approach might not be the best for

achieving the desired growth; rather an approach that involves openness to the use of the

platform in a way that was unintended is more likely to deliver sustained viral growth.

Limitations and Future Research

While the mixed methods approach attempts to make up for one methodology’s weaknesses with another’s strengths, it is not without limitations. In fact, in the mixed methods context, validity issues encompass all those of each method used (Johnson &

Turner, 2003). I will first address the limitations of each strand and then I will address the

limitations of the overall mix methods approach. Table 2 provides a summary of the

commonly agreed upon quality criteria for qualitative studies (Lincoln & Guba, 1985),

quantitative studies (Cameron, 2011), and mixed methods (Sale & Brazil, 2004) as well

as how I addressed these in this study.

57 TABLE 2: Quality Criteria

Criteria Addressed Through Qualitative Credibility Persistent observation and member checks Transferability Identical elements, thick description Dependability Saturation Confirmability Re-coding

Quantitative Internal Validity Covariance of IV and DV External Validity Use of two different respondent groups Construct Validity Conducted several rounds of instrument and item pretesting Convergent Validity CR > AVE, AVE > 0.50 Discriminant Validity MSV < AVE, ASV < AVE Reliability CR > 0.70, Cronbach alpha > 0.50 and factor loadings > 0.40 Objectivity Anonymity of respondents

Mix Method Truth Value Theoretical foundation Applicability Fit of methodology to research question Consistency Triangulation of results Neutrality Address research biases

Qualitative strand. In the qualitative strand I must worry about credibility, transferability, dependability, and confirmability. To address concerns of credibility, I recorded each interview, had them professionally transcribed, reviewed the transcript several times, and wrote summary notes for each. However, my ability to summarize accurately and interpret the interviewees’ moods is a limitation of the study. Questions of transferability and reliability could arise as there was only one researcher coding. I made use of a common set of codes generated from the first several interviews to address this issue. Additionally, as research that involves theory generation, thoughtful and thorough analysis combined with “thick descriptions” should be sufficient to address concerns with reliability (Silverman, 2006). Concerns over confirmability were addressed by re-coding several times and developing standard, reusable codes.

58 The sample for the qualitative study was sourced from the researchers’ networks and was limited to 29 SNS users. The second group of respondents included two former executives of Friendster, two current/former executives of Facebook, and one consultant who worked with both Facebook and Friendster. While the users and executives were representative of the larger population in terms of age, education, year joined and other demographics, the relatively small sample size compared to the overall population is still a limitation. There might be differences in the sample and the population in regards to the culture, customs, and norms given that the majority of the individuals were located either in the mid-west or west-coast of the USA.

Quantitative strand. In the quantitative strand, quality criteria required that I address several forms of validity as well as reliability and objectivity. As I operationalized the user behavior constructs of voyeurism and exhibitionism I adopted items from domains that are quite different from my study. I, naturally, attempted to limit respondents’ cognitive difficulties, including face validity, by conducting several rounds of pretesting using Bolton’s (1993) techniques, but the possibility for confusion remains.

To address external validity I sampled two different groups of respondents in different geographical locations and sourced from different networks. Comparing the results and finding that responses were similar demonstrated some degree of external validity or generalizability.

I tested for content validity by having researchers familiar with the field of study review the items; however, I did not test using the Lawshe formula (2006). Additionally,

I did not perform a Q sort to test for user intersubjectivity and agreement on item grouping. I did ensure that the items that were used to form the constructs had Cronbach

59 alphas over 0.60 and factor loadings above 0.40. For reliability I ensured that the composite reliability (CR) scores were greater than 0.70. However, some of my constructs had only two remaining items, which could call into doubt the construct validity. For convergent validity I ensured that average variance extracted (AVE) were greater than 0.5 and CR > AVE. For discriminant validity I ensured that maximum shared variance (MSV) was less than AVE.

An additional limitation of this research is social desirability bias. Individuals may have been reluctant to self-report voyeuristic or exhibitionistic behaviors due to the social stigma associated with these behaviors. I attempted to alleviate this by guaranteeing the anonymity of the survey respondents. Additionally, I used neutral, non- judgmental wording and avoided the use of the terms voyeurism and exhibitionism in the survey items. However, I did not use a randomized response method, as established by

Warner (1965), in which users are asked to flip a coin and answer the question truthfully if heads and untruthfully if tails, allowing a greater degree of anonymity.

Mixed methods research design. Quality criteria for mixed methods include addressing truth value, applicability, consistency, and neutrality (Sale & Brazil, 2004) as well as meta-inferential issues related to the integration of the qualitative and quantitative strands (Tashakkori & Teddlie, 2008). The truth value is a pragmatic rule where an expression is determined by the experiences of practical consequences and can be applied by thinking “what will happen if you do X” (Johnson & Onwuegbuzie, 2004). I addressed this in my study through the application of practitioner knowledge as well as theoretical underpinnings. However, I have attempted to bridge a theoretical gap by spanning multiple theories from different research domains to form a new theory. Because of this

60 ambitious undertaking my work is naturally subject to limitations identified by critics that fundamentally disagree with my cross discipline theoretical integration.

A limitation of the mixed methods approach in general is that the overall characteristic of the method is the degree to which the individual methods – qualitative and quantitative – are similar or different from one another. In certain circumstances methodological tools such as scaled questionnaire and structured interview could be considered similar (Greene et al., 1989). Methods that are biased in the same direction can undermine the triangulation logic and result in spurious inferences (Mark &

Shotland, 1987). Because some constructs included in the nomological research model were derived from the interviews there is bound to be a degree of similarity in my survey scales and my structured interview questions.

The applicability criterion of mixed methods stems from the fact that there are such a wide variety of combinations and sequences possible when considering qualitative, quantitative, and modeling strands. I have argued that my research problem is ideally suited for a mixed methodological study as it studies an emerging and new phenomenon in a context. I needed to use qualitative methods to sensitize the context of the phenomenon and to observe specific behaviors in that context. The analysis of the direct effects of individual and social constructs on the dependent variables of fan out and retention on a SNS was ideally suited for a quantitative analysis as it aided me in identifying which factors and in what amounts they offered explanatory power of viral growth. While I applied the insights gained from the qualitative study to help further explain my quantitative results, a limitation of this study is that I did not complete

61 another qualitative study at the end to gain further triangulate results and theory. I do believe that this could be a useful path for future study.

The quality criterion of neutrality is important for mixed methods studies as is objectivity of single method studies. A limitation of this and most research stems from the biases that the researchers bring to the study. While this is unavoidable, one way to address this is to simply acknowledge those biases (Strauss & Corbin, 2007). While it is not possible to identify all biases of the researcher one of the largest involves that of prior practitioner knowledge. As a practicing technologist I have a bias towards understanding and interpreting user interactions with technology based on my professional experiences.

For example, I expect that for the most part the functionality on most sites including SNS can be easily figured out by the majority of users. Therefore, during interviews of normal

SNS users, I was astonished at individuals’ inability to “use the functionality properly” and instead how they took different steps to achieve their desired results. Additionally, my individual bias for not sharing personal information with others was evident by my bewilderment over individuals sharing with 500 or more of their “closest friends” details of their excessive drinking, sexual escapades, or infidelity. While I attempted to temper my judgments about individuals, I am sure that my biases are evident in the descriptions of the codes and various other terminologies used to describe these behaviors.

Future research. Future research could address some of the limitations in my study by reproducing my results with a larger sample of geographically disperse and culturally diverse users of SNS as well as following my guidance above for enhancing the quality. Examples of this would be to have multiple researchers code the interviews

62 utilizing a code book and to integrate social desirability bias scales into the survey. These steps would strengthen the reliability and validity of the results.

Additionally, future research could look to combine factors from the two-sided

economic models such as price and cost with social exchange model. This would be

especially applicable to the ecommerce platforms that also have social aspects such as

ratings or forums. The application of these drivers is by design contextually specific and

future research should look to extend the application of these beyond viral growth of SNS

platforms.

One of the most important next steps for this research is to investigate the

evolutionary nature of the ratios of between users on SNS and types of features they offer. It has been implied from this study that both SNS and users evolve over time with regards to the ratio of voyeurism and exhibitionism. I suspect that early adopters of SNS start out as low exhibitionism and high voyeurism between users testing out the SNS,

gradually migrating towards higher levels of exhibitionism. Facebook started with very

limited functionality for creating a profile including name, email, profile photo (limited to

one), university, relationship status, etc.6 In Oct 2005, Facebook added the functionality for unlimited storage of photos with tagging of other users.7 This additional functionality

encouraged the increase in exhibitionism between users. This suspected evolution of the

ratios of voyeurism and exhibitionism between users along with the SNS features should

be confirmed and explored further.

6 See http://www.quora.com/Facebook-Company-History/What-were-the-first-features-of-a-persons- Facebook-profile-when-the-site-launched-in-2004

7 See http://www.huffingtonpost.com/2011/08/02/facebook-photos-infographic_n_916225.html 63 I conducted a post-hoc analysis of the social exchange and TAM models

moderated by the multi-group based on age with the results reported in Table 3. As

denoted by the Z-score the importance of the exhibitionism’s influence on co-creation and co-production becomes significantly more important as the users age and the perceived ease of use decreases to become non-significant. These results support not only my study’s results, but also the proposed idea of a dynamic nature of factors such as exhibitionism that influence the viral growth of SNSs.

TABLE 3: Model Comparison by Age

Younger (<29) Older (>=29) Model Path Z Score* beta SE Beta SE VOY -> CC -0.20*** 0.032 -0.28*** 0.045 1.45 EXH -> CC 0.39*** 0.035 0.51*** 0.047 2.05 Social EXH -> CP 0.39*** 0.019 0.29*** 0.025 3.18 Exchange CC -> CP 0.54*** 0.019 0.63*** 0.019 3.35 Model CP -> FO 0.41*** 0.033 0.42*** 0.034 0.21 CP -> RET 0.15*** 0.029 0.19*** 0.033 0.91 PEU -> RET 0.10*** 0.020 0.01(ns) 0.022 3.03 TAM Model PU -> RET 0.52*** 0.028 0.58*** 0.031 1.44 PU -> FO 0.46*** 0.028 0.49*** 0.031 0.72 R2 FO 0.50 0.56 DVs 2 R RET 0.59 0.61

*Z=

64 APPENDIX A: Alexa Ranking

Social Alexa Ranking Network (May 2010) Facebook 2 Friendster 987 LinkedIn 16 MySpace 84 Ning 261 Orkut 115 Twitter 9 Yahoo!360 3000 YouTube 3 Flickr 32 ShareThis 2392 Evernote 1719 Yelp 343

65 APPENDIX B: The Co-Production of Viral Growth: A Comparative Analysis of Two Social Networking Sites

Abstract

Theories of technology product adoption and diffusion fail to adequately explain viral growth or viral growth. Additionally, the role of product design in users’ propensity to use and recommend products is both understudied and poorly understood. To address a vexing gap in knowledge about what explains viral growth, we conducted ethnographic interviews with technology executives and users of two social networking sites that have experienced dramatically different growth patterns. Our findings reveal that product co- production and user self-identity -- not product attributes – fuel viral growth. Social networking products co-produced by users and providers, with “meaning” socially constructed by customers, permit users to more effectively establish and maintain their self-identities and are far more likely to result in viral growth than are engineering-centric products. User experiences with Facebook and Friendster and organizational responses to them demonstrate the role of self-identity – and in particular five specific selves – on viral growth.

INTRODUCTION

Market values and stock returns of internet businesses are directly impacted by user traffic (Trueman, Wong, & Zhang, 2000) which, in turn, can be maximized by viral growth or “viral” marketing strategies (Nam, Manchanda, & Chintagunta, 2009; Van der

Lans, van Bruggen, Eliashberg, & Wierenga, 2010). The impact of user traffic on value can be seen in the contrasting histories of internet social networking sites Facebook and

Friendster. Facebook, having achieved exponential user growth, announced expected

2010 revenues of $1 billion (Carlson, 2009) while Friendster, with less stellar growth, announced its acquisition by MOL Global for $26.4 million (Arrington, 2009).

One of the critical, but systematically understudied, issues related to the success of viral marketing efforts is viral product design (Aral & Walker, 2010). Lack of understanding by internet businesses about how product design can influence users’

66 propensity to use and recommend their products (Watts & Peretti, 2007) reduces the likelihood of exponential (i.e., viral) user growth, upon which internet business models that rely on advertising depend (Evans & London, 2008). Viral marketing refers to

“…any strategy that encourages individuals to pass on a marketing message to others, creating the potential for exponential growth in the message's exposure and influence”

(Wilson, 2000:1). Despite a plethora of research about it (Ferguson, 2008; Kalyanam,

2007; Phelps, 2005; Rosen, 2002; Walsh, Gwinner, & Swanson, 2004; Woerndl,

Papagiannidis, Bourlakis, & Li, 2008), viral marketing does not guarantee viral growth

(Ferguson, 2008). For every high profile example of a viral product that succeeds, there are many more unsuccessful attempts. Predicting success, according to Watts and Peretti

(2007:2), is “extremely hard, if not impossible − even for experienced practitioners.”

Prevailing theories of technology product adoption and diffusion fail to adequately explain viral growth because the technology rich environment that users exist in today (Gaskin & Lyytinen, 2010) needs to be understood as an evolving ecology of service, as created by users and service providers, rather than as a collection of independent tools for users to adopt (Jung & Lyytinen, 2009). The most widely accepted theories – Innovation Diffusion Theory (Rogers, 1962) and the Theory of Reasoned

Action (Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1975) as well as derivative theories including the Theory of Planned Behavior (Ajzen, 1991) and all versions of the

Technology Acceptance Model (Davis, 1989; Venkatesh & Bala, 2008; Venkatesh &

Davis, 2000a; Yi, Jackson, Park, & Probst, 2006) – have illuminated the nature of viral growth only coarsely (Van den Bulte & Stremersch, 2004) frustrating marketers, product managers, and researchers and leading some to deem diffusion model parameters as

67 informative rather than evidentiary of something specific (Stoneman, 2002). Due to the

combinatorial explosion of technology tools and functionalities, there are decreasing

returns from utilizing a static single tool/single user approach (Lyytinen, 2010).

To achieve viral growth or viral growth, existing users must be retained while

new users are successfully recruited (Penenberg, 2009) as measured over a period of time

(Jurvetson, 2000). Despite recent advances in the understanding of social capital in technology usage (Ellison, Steinfield, & Lampe, 2006, 2007; Steinfield, Ellison, &

Lampe, 2008) and the role of social factors in technology adoption (Davis et al., 1989;

Hartwick & Barki, 1994; Karahanna et al., 1999) there is a fundamental gap in our

knowledge about the attributes of products and services in which viral marketing works

for some but not for others (Subramani & Rajagopalan, 2003). We attempted to address

this gap by comparing and contrasting technology executives and users’ experiences on

two social networking sites – Facebook having achieved viral growth and Friendster

having failed to achieve it.

What attributes of internet based products and services, we asked, make them

more likely to achieve viral growth? In an attempt to identify these product attributes, we

reviewed the literature on viral growth and viral marketing and then conducted qualitative

semi-structured interviews with users and executives of two social networking sites that

had dramatically different results in terms of viral growth. Our findings reveal that

product co-production and user self-identity -- not product attributes – fuel viral growth.

Social networking products co-produced by users and providers allow users to more

effectively establish and maintain their self-identities and are far more likely to result in

viral growth than are engineering-centric products.

68 RESEARCH METHODOLOGY

Methodological Approach

We sought to discern and examine possible relationships between product attributes and individuals’ attitudes or behaviors that increased the Viral Coefficient (Fan

Out and Conversion) and Retention Rates of social network sites. Phenomenological, semi-structured interviews, informed by grounded theory principles of (Strauss & Corbin,

1990), were conducted with customers of Facebook and Friendster. Semi-structured interviews allowed for structure and uniformity in the collection of data but preserved flexibility and opportunity for the emergence of novel contributions from -respondents.

As described by Strauss and Corbin (1990:12), the qualitative method “…allows researchers to get at the inner experience of participants, to determine how meanings are formed through and in culture.” Our research problem was well suited for such an approach as it involved individual’s attitudes and behavior within a socially complex environment. Furthermore, retention of existing users as well as the fan out to and conversion of new users are often driven by factors that are very personal and evoke strong emotional reactions not easily extracted or measured by quantitative methods.

Our data was interpreted using analytical methods recommended by Strauss and

Corbin (1990) that included constant comparison and theoretical sampling. Emergent themes and concepts directed forward sampling which continued until no more themes or concepts could be identified, signaling theoretical saturation.

Sample

Because viral growth depends on the two criteria of usage and sharing (Jurvetson,

2000), our user respondents were selected on the basis of how much they used their social 69 networking sites and the extent to which they shared content and recommendations with others. The criteria for usage and sharing were adapted from statistics provided by

Facebook estimating average on site use of 55 minutes per day per subscriber (Facebook,

2010). Users self-classified themselves according to the amount of time they spent on the site and how often they shared or requested friends.

Our primary sample consisted of 29 social network site users − 19 Facebook subscribers and 10 members of Friendster. A second group of respondents included two former executives of Friendster, two current/former executives of Facebook, and one consultant who worked with both Facebook and Friendster. These individuals were interviewed to obtain information about the product development strategies of the two companies during various user adoption phases.

Our sample included heavy and light users and sharers for each site as shown in

Table B1 below. The user group consisted of 18 women and 11 men, aged 21 to 62 with education spanning high school to graduate school. All were US citizens and current US residents. All were sourced using the principle researcher’s personal and professional networks. The former employees of Facebook and Friendster (two directors and two executives) and the consultant, all males, were similarly sourced.

TABLE B1: Sharing and Usage Distribution

70 Data Collection

All 34 interviews were conducted during the spring and summer of 2010, 15 face- to-face and 19 by telephone. All interviews, averaging approximately 60 minutes, were digitally recorded (with the permission of the respondent), and later transferred to a computer and uploaded to a professional transcription service for processing into text form. Respondent confidentiality was ensured. Notes were manually taken and a memorandum summarizing each interview was written immediately afterward. Social network users were asked five questions, first to review their personal and professional backgrounds, then to recount their experiences with social networks in the real world such as with clubs, churches, or sports teams. Thereafter they were asked how they typically used technology to communicate with other people and to demonstrate to the researcher, and describe while they did so, how they typically use Facebook or

Friendster. Finally they were asked about the first time they used Facebook or Friendster.

Former and current employees of Facebook and Friendster were asked to narrate their personal and professional backgrounds, to describe how the company’s product strategy changed during their years of employment and if it changed when the company became aware of its potential viral growth. They were also asked to talk about product strategy and how ideas for new products or changes to existing ones were generated.

Data Analysis

Data analysis began after the first interview. All recorded interviews were listened to, transcribed, and the transcripts read several times. Thereafter we followed the recommendations of Strauss and Corbin (1990) and conducted three iterative phases of coding: open, axial and selective. During the first phase of open coding we examined

71 each transcript line by line to identify fragments of text. These fragments were labeled and cross referenced with excerpts from prior transcripts.

Open coding resulted in the identification of 534 codes later grouped into 24 categories relating to user retention, product attributes, new users joining, or strong emotion. During the second phase of analysis, axial coding, we refined these emergent themes by defining their properties and dimensions, a process that later reduced them to the 19 themes shown in Table B2 below. During the final phase of analysis, selective coding, we focused on 9 key themes, identified in Table B2 as “selected”, that yielded the five findings reported in the next section.

TABLE B2: Original and Selected Themes

Number Theme Selected 1 Association prestige of connecting with a special person * 2 Concerned about the lack of ability to use technology 3 Concerned about the security of private information 4 Conforming to etiquettes * 5 Continuum of intimacy using different technologies 6 Creepiness detracts from enjoyment 7 Ease of Use 8 Emotional reaction to technology 9 Live vicariously by using virtual reality * 10 Minimize isolation and feel connected * 11 Need to feel in control of the technology * 12 Peer pressure to join 13 Performance of site 14 Reputation Management * 15 Self-perception influences adoption and usage 16 Sharing fun and entertainment with friends * 17 Usefulness 18 Using technology to sustain relationships * 19 Voyeurism and Exhibitionism *

72 FINDINGS

Interviews with former executives of both Friendster and Facebook − and a

consultant who worked with each − revealed stark differences in the two firms’ early

phase strategies and visions. These differences impacted the viral parameters of

Retention, Fan Out and Conversion. These distinctions were also reflected in the findings

from subsequent − and similarly antithetic − narratives elicited from Facebook and

Friendster users. We begin this section by reporting findings from the interviews with

early managers followed by those derived from the narratives of recent users.

Executive Interview Findings

Former managers revealed that Facebook focused from inception on viral growth, adopting a strategy of relentless vigilance of its users. As one executive revealed,”…growing (was always) the most important thing.” To fuel growth, engineers observed and relied upon feedback from users for product strategy. Friendster executives, contrarily, described their company as adopting a strategy of engineering centric product development – neither informed by the user nor steered by a vision of viral growth. As one former executive admitted, “…nobody really knew what (viral growth) meant.”

Facebook managers described an integral part of the typical design process as “rolling it out to a subset of users and letting them play with it, then gathering their feedback” while

Friendster managers recalled “hiring engineers doing too much cutting edge stuff,” obsessed with “pushing the envelope in terms of new technology” and “running amok trying to solve problems that shouldn’t have been solved.”

73 Facebook executives emphasized the organizational learning that resulted from their focus on users – using terminology including learning, insight, observation and experimentation nearly twice as often as did Friendster executives.

Both of the Facebook managers elaborated on that firm’s focused feedback approach to monitoring customer actions (usage and sharing) – both during the original roll out of the social networking site as well as while new product features continued to be developed. The opposite was reported by the Friendster executives when describing

Friendster’s product development approach during years 2003 – 2006. They revealed a breach between product managers who requested specific features for customers and engineers who “had their own opinions” and “if they didn’t want to do something, didn’t.” The dramatic contrast in the foci of the two firms is clearly demonstrated in the interview quotes in Figure B1 below.

74 FIGURE B1: Contrasting Perceptions of Organization Culture & Product Development Focus

Facebook’s unrelenting focus on users extended to measuring their attitudes toward performance, advertising and security in addition to product features. Our respondents revealed that in support of user preferences Facebook product developers felt empowered to “…break every rule that you think you shouldn’t be breaking… get called out on it, and then…apologize, or…back off.” In contrast, Friendster executives, 75 describing the 2003-2006 period, documented that firm’s stubborn focus on fixing a specific technical feature (the f-graph) that caused performance problems long after it had been deemed redundant and abandoned by rival social networking sites. “We were spending so much time trying to solve that problem,” one Friendster former manager lamented. “It’s a very difficult engineering problem to solve − in the scale of exponential problems….Other (sites) were saying…‘we don’t need that stuff.’”

The two firms’ antithetical responses to user preferences were vividly exemplified in unsolicited reports by each set of executives about how they responded when customers independently created accounts for their pets. Friendster management acted to swiftly shut down the accounts while Facebook managers enthusiastically supported the development of dogbook and catbook applications, resulting, by 2010, in over 1 million pet “users.”

User Interview Findings

Data derived from interviews with 19 Facebook and 10 Friendster users corroborated the experiences reported by the companies’ early stage executives.

Facebook’s relentless focus of customer needs and purposeful attention to their feedback resulted in product features that our user respondents reported mattered to them – and that escalated their usage and sharing. Friendster’s comparative disinterest in the customer, on the other hand, thwarted viral growth. The two firm’s antithetical approaches to product development resulted in starkly different user experiences involving self expression and identity. Users’ ability to express their identity on Friendster was restricted whereas on

Facebook it was encouraged. The data revealed 10 behaviors that demonstrated this difference ─ establishing etiquettes, managing reputations, gaining prestige from

76 associations, sharing fun, living vicariously, renewing relationships, exhibiting voyeurism or exhibitionism, controlling diverse worlds, minimizing isolation, and expressing emotional intensity.

Behavior 1: Managing Reputations

Facebook facilitated user reputation management efforts while Friendster did not.

This is demonstrated by the reported frequency of users’ management activity. Fourteen of 19 Facebook respondents – but only five of 10 Friendster users documented participation in some activity that could be classified as reputation management such as not including birth year on profiles, censoring their own posts, belaboring the selection of their profile pictures, or looking up others user’s profiles. In describing the process of selecting her Facebook profile picture, for example, one user said “…I studied all the pictures, which one shows less wrinkles, which one doesn't look fat, which one whatever until I found a picture that I liked. And then I had to crop it…” Another Facebook user described how a trusting relationship was built online with someone they had never met in person. “So there was trust built up from that relationship because we were both known to each other a bit through the community…”

Friendster, contrarily, thwarted users’ reputation management efforts by, for example, shutting down accounts of users who created fake personas. “These are fakesters, they're not people. This is supposed to be for actual people, so let's get them off of there,” Friendster founder, Jonathan Adams was quoted by one of our executive respondents. More sympathetically, a Facebook executive observed “I still run across occasional people profiles that are actually of their cats or dogs or something like that.”

77 Comparing the number of individuals participating in reputation management

against the expected using a chi-square test showed asymptotic significance approaching

0.01, indicating that the frequency of observations are markedly different from the

frequencies that we would expect by chance. Comparing the frequency of observations between Facebook and Friendster revealed a statistically significant difference well below a 0.05 alpha level.

Behavior 2: Living Vicariously

Our data revealed greater “vicarious living” by Facebook than by Friendster users.

The former more frequently reported using their site to share in the experiences of friends and family with the effect of strengthening those relationships. As one Facebook user said about her children “I talk to them all the time but it is one thing talking to them…it’s another thing seeing pictures.” Another Facebook user told about her sister in Hong Kong

“She showed me her dog, Timmy, and they live on the 43rd floor overlooking Hong

Kong Harbor and so I’ve never seen her apartment but I have seen it through the lens of the camera.”

Seventy discrete observations of vicarious living were revealed in the narratives of Facebook users vs. only 13 in those of Friendster respondents. These experiences included viewing pictures and/or videos of others or reading stories provided by them. A t-test revealed just below 99% significance. Testing the count of users who mentioned one or more experiences of living vicariously through a social networking site against what would be expected by chance using a chi-square revealed an asymptotic significance of 0.001.

78 Behavior 3: Sharing Fun

Another way that users demonstrated their excitement over Facebook feature development was sharing amusing material with others. Fourteen of 19 Facebook respondents, while only 5 of 10 of Friendster users, indicated that they had shared with their network something they considered fun or “silly” such as a video, article, or picture.

Testing the number of users who discussed sharing amusing items to what would be expected by chance using a chi-square revealed an asymptotic significance of 0.001.

There was a difference in the means of the frequency of sharing fun from Facebook users

(2.4 x per user) with Friendster users (0.5 x per user) well below a 0.05 alpha level.

Behavior 4: Exhibiting Voyeurism & Exhibitionism

All of the Friendster executives discussed Facebook’s photo tagging feature, released in 2006, as spurring the site’s growth rate. A Friendster executive stated “… the ultimate genius in photos came from Facebook when they let you tag people in a photo and then send an email saying, ‘You've been tagged in a photo,’ which, of course, is an irresistible email.” The majority of all user respondents indicated that they had either enjoyed viewing information or pictures of their friends voyeuristically or they had participated in exhibitionism by purposefully sharing evocative or interesting content about themselves. Users of Facebook described their interest in being voyeuristic in statements such as “I love to look at everybody’s pictures, a little voyeuristic …” and “I like looking at pictures period.” They also enjoyed the occasional display of exhibitionism describing it as “… I like to see when people write about whatever pictures

I put up…I mean, I like for people to see.” One Facebook user, referencing her siblings,

79 stated “We have a lot to say, and we think it’s very important what we have to say, and we must say it, and there is a little bit of shock value, and a lot of honesty.”

There was a statistical difference in the means of the frequency of exhibiting voyeuristic or exhibitionistic behavior from Facebook users (3x per user) with Friendster users (1x per user) well below a 0.05 alpha level. Testing the count of users between

Facebook and Friendster who discussed this behavior to what would be expected by chance using a chi-square revealed an asymptotic significance below 0.001.

Behavior 5: Controlling Diverse Worlds

Our Facebook respondents described the site as a diverse world that included friends, family, classmates, co-workers and even former girlfriends/boyfriends that needed to be integrated or segregated. Eleven of 19 Facebook respondents indicated that they actively managed their diverse networks while only three of 10 Friendster interviewees acknowledged doing so. Facebook respondents reported appreciating the site’s privacy control feature. As one respondent remarked, “It makes you feel like you have really good control.” Another, describing the drama that can ensue over comments within mixed networks, stated “I now deal with that in my groupings.” The average number of respondents observations of attempts to control their different worlds on

Facebook was 2.4 while on Friendster it was only 0.5. This difference was statistically significant approaching 0.05. Comparing this count of observations from Facebook to

Friendster against what would be expected by chance using a chi-square revealed an asymptotic significance below 0.001.

80 Behavior 6: Expressing Emotional Intensity

As expected of users whose voices were being heard and whose needs were being

met, Facebook respondents used more positive verbiage to describe their experiences

than did Friendster respondents. Utilizing Linguistic Inquiry and Word Count software

(LIWC2007) the respondent’s interviews were analyzed for word usage by categories.

Facebook respondents had 59 counts, 0.1% of all their words used during the interview,

that were strong positive emotional words compared with only seven counts, 0.02% of

the total words, of Friendster respondents. The difference in means was statistically significant well below 0.05. For strong negative emotional words, such as swear words, the opposite was identified. Friendster respondents, by word count, averaged four times

the use of negative emotional words than Facebook respondents. This difference was

approaching significance below 0.05.

Facebook respondents interlaced quotes such as “I just love that” and “I love it

when I get comments” into their stories. They also told stories about how they

encouraged others to join such as “I sent them an email and it said ‘I think you should

join’…I pressured both of them to join. I said, ‘It’s awesome. It’s so much fun. You’ll

love it.’”

Friendster respondents used phrases like “it wasn't really as fun” to describe their

experiences. About sharing pictures on Friendster, one revealed, “…it’s just too much of a pain in the butt for me.”

Summary of Findings

Facebook and Friendster’s differing approaches to product development lead to very dissimilar user experiences as revealed in the six behaviors discussed. User

81 expressions of their identity on Facebook were supported and encouraged, as reflected by

an executive who stated “we see one of our core use cases [as being] identity and identity

management.” Facebook users confirmed the establishment of self-identity through

behaviors involving the development and maintenance of relationships such as reputation

management and living vicariously. “If MySpace is more about showing your

creativeness...,” a user explained, “Facebook allows you to establish your social identity

through relationships..." Several Facebook users confirmed the expression of identity in

the behaviors of sharing, controlling diverse worlds, and expressing emotional intensity

through the site. As one user noted, "…occasionally a conversation can get heated, and

maybe more heated than you wanted (it) to get, and there can be some side effects...when

people start getting their identity wrapped up in their position."

DISCUSSION

Our findings summoned a return to the literature to explore theories about user

involvement in developing products and the social processes associated with establishing

self-identity.

Co-Production, Social Construction, and Organizational Learning

There is a growing trend of involving the customer as co-producer of products

(Davidow & Malone, 1992; Etgar, 2008; Grönroos, 1993; Hoyer et al., 2010) and a

concomitant spate of research that seeks to measure the extra value such involvement

provides (Wikström, 1996). Co-production of new products is defined as an “activity in which consumers actively contribute and select various elements of a new product offering” (O’Hern & Rindfleisch, 2009: 4) and in which customers are directly involved in the product development cycle of design, production, marketing, and consumption.

82 (Schultze & Bhappu, 2005). This is a shift in perspective from creating value for the customer to creating value with the customer where the knowledge, resources and equipment possessed by the customers are complemented (Normann & Ramirez, 1993;

Wikström & Normann, 1994).

Peer production or social production are similar terms, coined by Benkler (2006), to describe a model of economic production in which large numbers of people collaboratively contribute to produce large and meaningful projects. It is believed that firms that manage co-production most effectively will achieve a sustainable competitive advantage (Prahalad & Ramaswamy, 2004).

Lusch and Vargo (2006a) make a distinction between co-creation of value, which takes place in the consumption phase, and co-production, which takes place during the design phase. The process of new product development, in which both co-creation and co-production take place, is described as a sequence of operational activities linked in a network chain (Achrol & Kotler, 1999) with each set of activities – Design and

Development, Production, Marketing and Consumption – leading to the next (Porter, 1998).

Degree of co-production is a function of both the scope, the propensity of firms to collaborate with customers across all phases of the product development cycle, and intensity, the extent to which firms rely on collaboration within a particular stage (Hoyer et al., 2010).

Social production, or the co-production of products, and social construction are two mutually complementary perspectives where the goal of social production is the physical creation of product while social construction is the transformation of the product through social exchanges and daily use (Low, 1996). Social construction has been applied

83 very broadly to diverse subjects such as dementia (Harding & Palfrey, 1997), expertise

(Savage, 1996), and, one of the earliest applications, reality itself (Berger & Luckmann,

1967). It is theorized that labels and meanings we use to explain our world are dictated by

the most widely held beliefs to which we have been subjected (White & Epston, 1989)

and through participation in particular social groups (Speed, 1991). “Habitualization”

happens when an individual develops a process for dealing with a repeated situation;

whereas institutionalization, which is more akin to social construction, occurs with

reciprocal “habitualization” among many individuals (Berger & Luckmann, 1967).

Through meaningful activities, such as customer usage or engineering development,

humans define how products are to be used (Van Maanen, 1979) and this is shared with

others through social interactions (Berger & Luckmann, 1967).

While co-production adds value to the product through collaboration (Wikström

& Normann, 1994), social construction adds meaning to the product. Customer

participation in social production is optional but involvement in social construction

occurs automatically through interaction with the product. Social interactions affect the

way customers apply meaning to products, interact with them, and communicate about

them (Perret-Clermont, Perret, & Bell, 1991). Thus, the term social construction may be applied to the phenomenological and symbolic experience with products as mediated by social exchanges (Low, 1996).

Our data reveals Facebook and Friendster as strikingly different organizations in

terms of strategic focus, culture, and practices. Facebook actively sought the value added

from co-production of their product with customers. This resulted in a customer centric

social construction of their product’s meaning. Friendster, in contrast, discouraged co-

84 production, seeking no external input. This resulted in an internally established social construction of their product, where the engineering team attempted to impose meaning and preferred manner of use of the product on the customer.

Friendster, from inception, was preoccupied with solving technically challenging problems, paid little attention to user feedback and mimicked rather than innovated product features. Believing that the F-graph or friend-graph was the most important feature for establishing a social network, Friendster engineers refused to work on other projects and continued supporting the feature despite its negative impact on customer experience. Customers who attempted to institutionalize nonconforming usage of

Friendster’s product, such as creating false personas or accounts for their pets, suffered accounts termination.

Facebook steadfastly focused on viral growth, created a culture of pushing the limits of product feature acceptability, and consistently monitored customer usage.

Customers provided Facebook with feedback through their usage as well as verbally. As new products were launched, the viral growth team studiously monitored usage and sharing statistics to ensure features were not inhibiting the way users desired to use the product. When conflicts arose even between revenue generating features such as advertisements and the goals of viral growth, the advertisement features lost and were pulled from the pages.

Facebook also continuously pushed the boundaries of acceptability in terms of security, privacy, and technical innovation. There have been numerous criticisms about

Facebook’s privacy controls and policies. Their approach has been to find what is acceptable to their customers by testing the boundaries and being quick to back off when

85 they cross over the line of tolerability with their customers. In terms of technical innovation, the feature allowing users to tag other users in photos was seen by many as groundbreaking in terms of driving continued usage.

The result of these contrasting approaches to social production and social construction of products yielded dramatic differences in organizational learning within the firms and product performance. Snyder and Cummings (1998) argue for an organizational learning model that combines the perspective that organizational learning is the cumulative of individual learning (Argyris & Schön, 1997; Senge, 1990) with the perspective of organizational learning in terms of processes (Huber, 1991), policies

(Cole, 1993), and even cultures (Cook & Yanow, 1993). The resulting organizational learning model explains the affect on organization performance outcomes, such as productivity and customer satisfaction, through the mediation of organizational knowledge, as called for by Kogut and Zander (1992). It consists of four interrelated processes originally suggested by Dewey (1933) and built upon by others (Argyris &

Schön, 1997; Senge, 1990) – discover dissonance between desired and current state; invent appropriate solutions to alleviate the gap; produce solutions; and generalize learning to similar situations. Successful learning occurs when organizations complete all four processes in any order and often overlapping (Snyder & Cummings, 1998).

Facebook’s concerted focus on viral growth and co-production resulted in organizational learning where the dissonance between desired and current state was articulated in terms of customer retention and recruitment of new users (fan out). When solutions to fill this gap were identified and produced, the Facebook team monitored to ensure success and generalized the learning across other features within the product.

86 Friendster’s engineering centric focus on solving technically challenging problems resulted in an organizational learning where the discord between current and desired state was thought of in terms of technological solutions. The resolution of these incredibly challenging technical issues took significant amounts of time and resources which prevented development of customer desired features. While the Friendster engineers learned how to solve technically challenging problems these produced very little added customer value.

Stretching the boundaries of acceptability through innovative customer features allowed for continuous customer response which resulted in high retention rates of existing users and successful recruitment of new users (viral coefficient). The viral coefficient of Facebook as calculated from cumulative user data (Facebook, 2010) over a

30 day period, assuming a 100% retention rate, is shown in Figure B2 below.

FIGURE B2: Viral Coefficient of Facebook

Self-identity and Viral Growth

Facebook – but not Friendster – encouraged users to actively express their identity. In doing so, they seemed to establishing and maintaining five of the many selves that according to Prus (1997) make up one’s self-identity. This indicates that what users 87 sought most was the ability to create and maintain their self identities from the products

of the social networking sites.

Ashmore and Jussim (1997) suggest that self-identity theory owes its origin to

William James’ (1890) book The Principles of Psychology and his student Calkins’

(1900) paper “Psychology as science of selves” which identified the consciousness of

self. James (1890) conceived of the "empirical self" as consisting of the material, social,

and spiritual self. A further division of this social self is reflected in his oft cited

statement that an individual "has as many social selves as there are individuals who

recognize him" (James, 1890:294).

In psychology, Erikson (1950) proposed an ego-identity developed in adolescence

which Gleason (1983) argues was the ground work for psychologists to view identity as

internal to the person and persisting through time whereas the two sociological traditions

conceived of identity as social and variable. Gleason suggests the sociological path to

current day concepts of identity started with symbolic interactionist originated by

Cooley’s (1902) Human nature and the social order and later Mead’s (1934) Mind, Self,

and Society, both emphasizing the central role of social factors in development of self-

concept. Building upon Mead’s work, Foote (1951) proposed role identification as the mechanism and motivation by which individuals gained a socially prescribed role, which was the ground work for role-identity models such as Gecas’ (1982) structure and process streams, Scheibe’s (1985) social roles, as well as McCall and Simmons (1978) and

Stryker’s (1980, 1987) development of the multiple-role frameworks of identity.

The focus on the multiplicity or multidimensionality of the self-concept or self- identity has led to the realization that there must be an awareness of the accessible or on-

88 line self-concept (Cantor & Kihlstrom, 1987; Markus & Nurius, 1986; Markus & Wurf,

1987; Rhodewalt, 1986). The multitudes of selves that represent one’s self-identity are not all available at the same time but rather are best understood as a shifting array of accessible selves (Aral & Walker, 2010). Two different yet strongly related branches of identity theory have developed (Stryker & Burke, 2000). The first is reflected in the work of Burke and colleagues (Burke & Stets, 1999; Stets & Burke, 2003) which focuses on the internal process of self-verification; the second is demonstrated in the work of Stryker

(1980, 1987), focused on the linkages of social structures with identities.

Prus (1997), viewing community life as a sub-cultural mosaic that is continuously in the making, theorizes that the achievement of intersubjectivity is the primary enabling feature of human communities, and as Blumer (1969) stresses, the self is an emergent process that must be understood within the community, interacting with others, because it only achieves its central existence in situated activity. Prus (1997) acknowledges people’s concerns, images, and actions as they apply to the physiological and imaged selves while other selves (including ownership, proficient, accomplished, relational, integrated, isolated, helping, receiving assistance, entertaining, entertained, influential, receptive, vulnerable, and resilient selves) overlap with the physical embodiment but also focus on other aspects of the human condition.

Our study suggests not only that self-identity is an additional predictor within the domain of viral growth but that five specific selves are the most important in the prediction. From our study, the means of the number of observations for each social process were analyzed to determine if a statistically significant difference existed between Facebook and Friendster users. The results revealed that only two themes

89 (Establishing Etiquettes and Association Prestige) are not different between the two groups at above a 90% confidence, see Table B3 below. However, Association Prestige, when analyzed by heavy or light sharing activity demonstrated significance above 99%, indicating that it does help to explain the amount of sharing done by users regardless of the social networking site. In Table B3, we have also mapped the social process to the variable in the viral equation of cumulative users (Fan Out, Conversion Rate, or

Retention) that it most influences. A discussion of each of the selves follows.

TABLE B3: Social Processes of Selves, Viral Equation, and p-values

Social Viral Equation P(T<=t) Self Process Variable two-tail Establishing Etiquettes Retention 0.245 Imaged Reputation Management Fan Out 0.037 Association Prestige Conversion 0.225 Sharing Fun Conversion 0.024 Living Vicariously Fan Out 0.016 Relational Sustaining, Terminating, Renewing Retention 0.076 Relations Entertained Voyeurism & Exhibitionism Conversion 0.026 Controlling Diverse Worlds Retention 0.053 Integrated Reputation Management Fan Out 0.037 Isolated Minimizing Isolation Retention 0.063

Imaged self. According to Prus (1997) people attempt to foster consequential self- image through ownership, expertise, and relationships. They develop the imaged self by establishing proficiencies in using technologies and learning to manage both relational and isolated aspects of self. The three behaviors or social processes for developing the imaged self as witnessed through the interviews were establishing etiquettes, reputation management, and association prestige. Of these three, the frequency of reputation management was the most markedly different for Facebook and Friendster users.

The social process of establishing etiquettes within the usage of social networking sites is a similar concept to the establishment of dominant analogies from social

90 construction. As mentioned in the above social construction discussion some individuals are more influential than others in determining what and how products will be used.

Relational self. Prus (1997) describes the relational self as the desire for and the openness to affiliation. Technologies, such as social networking sites, which allow the selection of others as friends and the strengthening of their ties, foster affinity within groups. Yoels and Clair (1995) explain that affiliation can be fostered by the use of humor between people but humor may also serve to disaffiliate users if not managed to the social norms and etiquettes (Bjorklund, 1985). Interviewees often shared humorous videos, pictures, or status updates among friends while the receivers of these described these as opportunities to connect more closely by feeling they were participating in the events pictured. The social processes associated with the relational self include sharing fun, living vicariously, and maintaining or terminating relationships.

The social processes such as sharing fun, which are important to the development of the relational self, are subject to norms and etiquettes (Bjorklund, 1985). Therefore, the relational self is influenced by the establishment of dominant analogies as found in social construction.

Entertained and entertaining self. The roles of the entertained and entertainer are interlinked by mutuality of interaction but in some cases people choose exclusively the performer or audience roles in which case there is not an expectation of reciprocity (Prus,

1997). The entertaining self must develop their ‘voice’ based upon their audience’s interest and at times must deal with difficult or upset audience members. The entertained self will seek out the entertainers, revisit frequently enough to consume the entertainment, and lose/regain interest. Eastin and LaRose (2000), in studying Internet

91 self-efficacy identified that perceived social, informational, and entertainment outcome

expectancies are increased by self-efficacy.

Integrated self. With the advent of smart phones and Internet-enabled handheld

computers there is a further blurring of the traditional family–work boundaries

(Schlosser, 2002). Often the interviewees expressed that they had mixed their networks of

friends, family, and work on the social networking sites. This integration of different

worlds required control and reputation management in order that they coexist.

As with the development of the relational self, the social process of controlling

diverse worlds, which is important to the development of the integrated self, is subject to

norms and etiquettes. Therefore, the integrated self is also influenced by the

establishment of dominant analogies as found in social construction.

Isolated self. Interviewees often described feelings of rejection or aloneness if they failed to spark a conversation with a topic they shared. Addressing feelings of

isolation while adapting to the ‘integrated self’ and developing a ‘relational self’ may

cause conflicting choices (Schlosser, 2002). Prus (1997) suggested that people feel

isolated when unable to establish a desired affinity.

The observation counts of all the identified social processes were grouped by

related self and graphed, see Appendix A. This revealed a distinct clustering of

observations for the Facebook and Friendster groups on the Relational, Integrated, and

Entertained Selves. The Isolated and Imaged Selves had overlapping observations. These results indicate that the product affordances for the individual to experience the

Integrated, Entertained, and Relational Selves are most important in predicting viral growth of social networking sites.

92 A two-dimensional scaling of all nine themes was performed separately for the

Facebook and Friendster user data. The result was a stress = 0.09284 and R2 = 0.95978, which is above the ideal of 0.90 for non-metric multidimensional scaling, see Appendix

B. Dimension 2 (y-axis) indicates the longevity of a relationship where positive = sustained relationships (lifelong friends) and negative = sporadic relationships (a buddy to whom one sends jokes back and forth). Dimension 1 (x-axis) reflects the formality of relationships, positive = formal (etiquette and reputations) and negative = informal

(sharing and pictures).

These dimensions imply that Friendster’s users relational selves were concerned with more formal relationships (stronger ties) and Facebook users relational selves experienced more informal relationships (weak ties). This explains the differences in network sizes, Friendster networks being in general much smaller than Facebook networks, which directly relates to the fan out rate of the viral coefficient. Additionally, the imaged selves of Friendster were focused on shorter term relationships such as someone new to date, while Facebook’s imaged selves involved longer term relationships such as reconnecting with friends from high school.

A Grounded Model for Viral Growth

Combining the concepts of evolutionary social construction and self-identity, we create a grounded model for viral growth that is presented in Figure B3 below. The main logic of the model is that the establishment or maintenance of self-identity increases usage as well as the intention and behavior of sharing. This is observed by the organization and integrated into the product development cycle as new features, which allows the users to express their self-identity more.

93 FIGURE B3: Conceptual Model of Viral Growth

IMPLICATIONS FOR PRACTICE AND FUTURE RESEARCH

Our findings should be of keen interest to internet based companies who hope to

achieve viral user growth of their products and services. Whereas many companies rely

solely on marketing to achieve viral growth, we demonstrated that sustained viral growth

depends on characteristics of products and services that facilitate users experiencing

social processes that establish and maintain one’s self-identity. Our research suggests that these characteristics are not likely to be built directly through internal design or engineering but rather must be created by the firm actively soliciting customer feedback, often through the monitoring of usage patterns rather than direct communication. A failure to monitor the cues of customers will result in a product that does not facilitate the social processes of establishing and maintaining one’s self-identity resulting in lower usage and sharing. Marketing without this open feedback mechanism can boost viral

94 spread of a marketing message, but often results in the “Slashdot effect”, a traffic spike

that is not sustained.

We believe our findings may be important not just for social networking sites, but

also for other internet based businesses and possibly real world consumer technology

businesses ranging from televisions to cell phones. Understanding the factors that explain

usage and sharing of a technology will allow product and services to be developed that

are more likely to achieve viral growth and better fulfill customers’ needs.

The results of this research should also be of significance to academics who study

or research technology adoption. Our findings demonstrate that the ability of a user to

experience the social processes that help establish and maintain self-identity predicts whether he/she will continue using or share recommendations about a particular technology product or service. Recent empirical investigations have provided insights about the impact of social factors on technology acceptance but there is a lack of knowledge about the influence of products and services characteristics in successful viral marketing campaigns (Subramani & Rajagopalan, 2003) and specifically the characteristics of products and services having to do with self-identity (Lee, Lee, & Lee,

2001).

While previous research shows that attitude predicts user behavior (Davis et al.,

1989), our research suggests that focusing on factors that influence attitude is important but not sufficient. Prediction of technology adoption must include factors such as self- identity that influence behavior directly. According to Sparks and Shepherd (1992) and

Terry et al. (1999) there exists an independent effect of self-identity predicting behavior, not attitude, even when a measure for past consumption is included.

95 LIMITATIONS

Our respondents were selected from a relatively small network of referrals and

many of the respondents had similar backgrounds. The sample was small, non-random,

and not sufficiently diverse to allow generalization across larger populations. Therefore

our findings are suggestive, but not conclusive. Future research should include a broader

representation by age, education and socio-economic status. A broader sample with more

diverse educational socioeconomic backgrounds might have yielded different results. A

larger sample size might have distinguished affects of gender. Although conscious effort

was expended to minimize potential bias resulting from the principal researchers’

considerable experience and expertise in the internet field, the possibility of such bias

must be noted.

Because of similarities in product offerings and time to market only two social

networking sites were selected for this research, Facebook and Friendster. Inclusion of other and different internet based products and services might have produced different results.

96 FIGURE B4: Observation Counts of Social Processes Graphed By Selves

97 FIGURE B5: Two-Dimensional Scaling of Social Processes

98 APPENDIX C: The Antecedents of Viral Growth on Social Networking Platforms

ABSTRACT

Social networking platforms, systems designed to provide digital content services specifically for social network sites, continue to develop through a rapid combination of components forming a service ecology that is much more than a single tool or service. In spite of this development, the most widely utilized theories of technology adoption and usage have focused on single user level tool adoption which limits their explanatory power of services platforms. These platforms have experienced tremendously rapid growth rates and the current state-of-the-art research attempts to explain this phenomenon through an economic or network effect lens, which fall short in explaining individual or social antecedents driving this phenomenon. Filling these gaps, the present study identifies social and technological factors that influence widespread and fast adoption of digital services on social networking platforms. Our findings suggest that (a) perceived usefulness has a strong, positive effect on predicting two critical elements of viral growth – fan out and retention, and (b) individual behaviors of voyeurism and exhibitionism and the platform processes of co-creation and co-production provide a stronger explanation of viral growth on social networking platforms than single-user focused technology adoption theories. This study makes an important theoretical contribution by articulating the impact of social factors on fan out and retention on social networking sites thus offering new vistas to examine digital platform growth and the diffusion of digital services.

INTRODUCTION

Information technologies that have been viewed as isolated systems are increasingly being seen as components of infrastructures offering a growing variety of services (Mathiassen & Sørensen, 2008). Social networking platforms are an example of such infrastructural technologies. These platforms help build and sustain social networks defined as “a social structure of nodes that represent individuals (or organizations) and the relationships between them within a certain domain” (Liccardi et al., 2007:225). We define accordingly a (digital) social network platform – or a social networking platform

for short – as an evolving IT system designed to provide digital content services that

represent individuals (or organizations) and the relationships between them within a 99 certain domain. With this definition we concur with Mak (2008) that software products

distributed on tangible devices such as CDs or DVDs are considered “goods,” whereas

digital content distributed over the Internet are considered “services” – such as e-books

(Hillesund, 2007) and online videos (Oh et al., 2008). Overall, social networking services are geared towards using digital information and its manipulation on the platform to identify, record, represent, and facilitate relationships between individuals by sharing,

organizing, and manipulating information about these individuals, their common

activities, or interests.

Social networking platforms must include a set of stable components that support

a continual assortment of services with the ability to morph into new services, and be

founded on an organizing architecture that integrates services, processes, and

technologies that enable social interactions and their growth (Baldwin & Woodard,

2009:19). Therefore, social networking platforms must be founded on an organizing

architecture that integrates services, processes, and technologies that enable social

interactions and their growth. Examples of such platforms are Facebook8, MySpace9,

Orkut10, Ning11, Twitter12, and LinkedIn,13 among others.

Some of these platforms have grown at an exponential rate. Moreover, some

social networks and related digital platforms have become omnipresent in individual and

organizational life (Agarwal, Gupta, & Kraut, 2008). The social groups built around these

platforms influence how and with whom we work and communicate, shape how we play

8 http://www.facebook.com/ 9 http://www.myspace.com/ 10 http://www.orkut.com/ 11 http://www.ning.com/ 12 http://twitter.com/ 13 http://www.linkedin.com/ 100 or entertain, and so on (Breslin & Decker, 2007). As a result, radically new forms of social networks and social interactions have emerged. The rapid growth of services has also kindled a desire for deeper scientific understanding of service evolution, platform growth and their antecedents (Chesbrough & Spohrer, 2006) generating new research challenges on IT based services inviting multidisciplinary approaches to characterize the nature, positioning, diffusion, and effects of services (Parameswaran & Whinston,

2007a).

An issue that has attracted the interest of scholars and practitioners concerns the antecedents that influence the fast, exponential growth of platform and related service

(Van der Lans et al., 2010). This phenomenon has been coined social contagion (Aral &

Walker, 2010). Indeed, as some have suggested more research is needed on a variety of topics including growth of new products, organizing innovation, and strategies for entry

(Hauser, Tellis, & Griffin, 2006). Though there is some research in economics (Van den

Bulte, 2010; Van den Bulte & Lilien, 2001; Van den Bulte & Stremersch, 2004) and network studies (Abrahamson & Rosenkopf, 1997; Enders et al., 2008; Hamm, 2008) which have sought to explain this phenomenon, the topic has remained largely unexplored. Moreover, around these studies there is much conceptual fuzziness, as they primarily focus on how economic exchanges and associated positive network effects drive growth (which is not the primary motivation to use social networking sites), or they have analyzed structural, network related factors of growth without explaining what individual, technological, or social antecedents might drive this growth (outside of economic value). Specifically, research is underdeveloped in this area. Seeking to reduce these theoretical blind spots, this study examines what social and individual factors drive

101 viral growth and it develops theory surrounding the understanding of service evolution,

platform growth and their antecedents.

Services on social networking platforms result from the incessant combination of

a wide variety of platform components triggered by significant contributions from and for

the benefit of the users (customers) (Vargo, Maglio, & Akaka, 2008). Yet, the most widely utilized theories of IT adoption and use, such as the Technology Acceptance

Model (Davis, 1989; Venkatesh & Bala, 2008; Venkatesh & Davis, 2000b; Yi et al.,

2006) have focused on explaining the adoption of individual and largely static tools.

There are no studies, which examined the extent to which such individual focused adoption factors truly explain viral growth.

Social networking platforms form an evolving service ecology which are not restricted to a single tool use by an individual user (Jung & Lyytinen, 2009). This has lead marketers, product managers, and researchers to deem diffusion model parameters as informative rather than evidentiary (Stoneman, 2002). Accordingly, individually focused acceptance models may have limited power to explain the adoption and continued use of services that result from a stream of new combinations of technology functionalities which support social interactions, or benefit from network effects related to collective use

(Lyytinen, 2010; Valente, 1996). To explain the contagion, we need to go beyond individually focused psychological explanations. In this study, we draw upon service- dominant logics of value exchange (Vargo & Lusch, 2004). This logic forms a radical departure from a goods-dominant logic, and argues that value is created with and determined by customer (user) behaviors during the creation and consumption of content

(Lusch & Vargo, 2006b). Accordingly, we are concerned with the antecedents of user

102 involvement in the constant creation of service content (co-creation) and in the ‘evolving’ of the platform services (co-production). Currently, there is a dearth of research on the effects of co-creation and co-production on service growth on social networking sites nor do we know what drives such processes.

The arguments advanced here highlight three focal research questions:

1. To what extent does the technology acceptance model explain viral growth?

2. How do the processes of co-creation and co-production affect viral growth on

social networking platforms? What technological, individual, or social

antecedents drive these processes?

3. To what extent do individually based psychological factors explain more of

the viral growth versus factors that drive social exchanges related to co-

creation and co-production?

The remainder of the paper is organized as follows. Drawing upon relevant literature, we review research on digital platforms, viral growth, and theories of technology adoption and diffusion. We theoretically develop and empirically validate three complementary research models that explain viral growth 1) technology acceptance based model with new antecedents derived from technology features relevant for social networking; 2) a social exchange-based model for digital service diffusion based on models of co-creation and co-production and technology features relevant for social exchange, and 3) a hybrid model which integrates models (1) and (2) with additional interactions. We believe improved predictive and explanatory capabilities can be achieved by combining individual technology adoption models and social exchange models.

103 The hypotheses are validated by conducting a survey that examined social networking use, adoption and their antecedents. A cross sectional self- administered survey was received from1449 users representing responses to usage on eight different social network platforms. The findings provide evidence that 1) perceived usefulness positively and strongly predict fan out and retention, two critical elements of viral growth; 2) individual behaviors driving social exchanges, voyeurism and exhibitionism, and platform processes, co-creation and co-production, provide greater explanatory benefits to the viral growth than a single user based technology adoption model alone.

The paper concludes with a discussion of implications, limitations, and potential avenues for future research.

MODEL DEVELOPMENT AND HYPOTHESES

In this section we will develop fully a set of models with associated hypotheses that explain fan out and retention in social networking platforms from individual technology adoption and social exchange perspectives. To this end we will organize the section as follows.

We will first provide an overview of how the individual adoption factors are expected to affect viral growth. To this end we use TAM constructs which are augmented with specific technology features that are critical in making social networking platforms user friendly and credible. Next we will explore how the user related and platform related factors that influence ways of expressing self-identity can affect viral growth. We propose an alternative model of social exchanges on networking platform and predict effects of co-creation and co-production on fan out and retention. Lastly, we will integrate the two models and observe some additional interactions between included

104 constructs in order to more fully explain what drives viral growth on social networking platforms.

The Impact of Individual Adoption Factors on Viral Growth

TAM is one of the most widely-employed models of individual acceptance and use of computing technologies. The model was initially developed and tested in the 1980s

(Davis et al., 1989; Venkatesh & Davis, 2000b). Subsequently, the model has been extensively validated across a variety of settings and subjected to theoretical extensions

(Venkatesh et al., 2003). It has also received substantial empirical support (Adams et al.,

1992; Agarwal & Karahanna, 2000; Venkatesh et al., 2007; Venkatesh et al., 2003) and has been found to consistently explain around 40% of the variance in individuals’ intention to use and a significant portion actual usage of information technology

(Venkatesh & Bala, 2008).

Venkatesh and Davis (2000b) proposed TAM2, an extension to TAM, that theorized the general determinants of perceived usefulness – subjective norm, image, job relevance, output quality, result demonstrability, and perceived ease of use – and two moderators – experience and voluntariness. Venkatesh and Bala (2008) theorized TAM3 as an integrated model of technology acceptance representing the determinants of individuals’ information technology adoption and use. TAM3 was constructed by combining TAM2 with the model of the determinants of perceived ease of use

(Venkatesh & Davis, 2000b).

The technology acceptance model (TAM) suggests that perceived usefulness and perceived ease of use are beliefs about a new technology that influence an individual's attitude toward and use of that technology (Davis, 1989; Davis et al., 1989). These have

105 often been amended with personal level factors such as self-efficacy or social factors such as group pressure or norms (Venkatesh et al., 2003). Due to the generic nature of these factors strong criticisms have been leveled against the model’s lack of “actionable guidance” of how to promote technology adoption in specific situations (Lee et al., 2003).

As a response, researchers have introduced context-specific antecedents to the TAM including e.g. e-mail features (Karahanna et al., 1999) and e-commerce features

(Koufaris, 2003). Therefore further refinement of antecedents by integrating the determinants of perceived usefulness and perceived ease of use to account for user acceptance, appear to also hold the key in explaining the adoption of services associated with digital platforms (Delone & McLean, 2003; Rai et al., 2002; Venkatesh & Bala,

2008).

The practical utility of considering TAM stems from the fact that social network platforms are heavily technology-driven and considered as a type of innovative technology. Thus, it is fitting to explain viral growth on social networking platforms. The proposed illustrative framework, shown in Figure C1, is a literature-driven arrangement of how TAM is employed to describe and predict adoption (fan out) with social networking platforms.

106 FIGURE C1: TAM Model of Viral Growth (Fan out / Retention) in Digital Platforms

Antecedents of fan out and retention. The original TAM3 model has single a dependent variable: use behavior predicted by behavioral intention (Venkatesh & Bala,

2008). In order to investigate use behavior in finer granularity on social networking platforms we have divided this determinant into two constructs: fan out and retention. In the viral growth parlance use behavior is thereby divided into continued usage or retention as well as fan out or encouraging others to join.

The antecedents that we place under the nomological structure of TAM, perceived usefulness and ease of use, are posited to predict fan out and retention behavior. Since user behaviors, such as retention and fan out involve technology use, it is reasonable to expect the variables of the technology acceptance model can be used to predict use behavior. For example, Gefen and Straub (2000) examined the effect of perceived ease of use on e-commerce acceptance, while Moon and Kim (2001) examined the effect of perceived usefulness and ease of use on consumer use of the Internet.

Although TAM was originally intended to predict technology use in the workplace, nonetheless the TAM variables can be employed to predict individual’s use of 107 social networking platform services. The technology acceptance model proposed that two key variables (i.e., perceived usefulness and perceived ease of use) influence the acceptance of Internet technology. Previous research has consistently argued that there is a positive relationship between perceived usefulness and perceived ease of use with acceptance of information technology, (e.g., Dishaw & Strong, 1999; Gefen & Straub,

2000; Venkatesh & Davis, 2000b) and this finding has also been validated in Internet technology use (e.g., Gefen, Karahanna, & Straub, 2003; Lederer, Maupin, Sena, &

Zhuang, 2000; Moon & Kim, 2001). The general premise is that both perceived usefulness and ease of use directly influence intention, but perceived ease of use also acts indirectly through usefulness (Davis, 1989). Further, Gefen and Straub discuss this relationship showing that perceived ease of use should affect use intentions through perceived usefulness (Gefen & Straub, 2000). Users of social networking platforms that perceive the services as useful and easy to use will affect their retention and fan out behaviors.

The previous arguments lead to the following hypotheses:

H1a: Perceived usefulness (PU) has a positive effect on fan out (FO).

H1b: Perceived usefulness (PU) has a positive effect on retention (R).

H1c: Perceived ease of use (PEU) has a positive effect on fan out (FO).

H1d: Perceived ease of use (PEU) has a positive effect on retention (R).

Antecedents of ease of use and perceived usefulness. Consumer’s perceived risk of online privacy and security has been shown to deter use of ecommerce services unless they perceive the service as a reliable milieu. Research reports a positive relationship between consumers’ levels of internet experience, perceived risks, and online activity as

108 crucial elements of e-transaction (Miyazaki & Fernandez, 2001). The presence of an

Internet security and privacy statement or an indication the site is a trusted source have

been found to increase anticipated disclosure and patronage rates for consumers with

relatively high online shopping risks (Miyazaki & Krishnamurthy, 2002). Therefore, we

posit that privacy policies of the social network platforms will affect the perceived

usefulness of the platform. Thus we hypothesize:

H2a: The presence of Privacy Policy (PP) has a positive effect on Perceived usefulness (PU).

Even the slightest slowness in the download of web pages, for example, as little as

400ms latency, have been shown to significantly reduce the number of Google searches

that users perform (Brutlag, 2009). Similar findings have shown reduced ecommerce site

browsing when delays exceeded 4 seconds (Galletta, 2002). Consequently, we believe

that the page load time of social networking platforms will affect the users’ perceived

usefulness of the platform. We propose:

H2b: Reduced Page load time (PLT) has a positive effect on Perceived usefulness (PU).

The impact of the clear presentation of system features on perceived ease of use

has been well documented in the past research (Benbasat et al., 1986; Dickson et al.,

1986). Users will attend to different aspects of the display in different ways and “traffic and sales are adversely influenced by poor interface features” (Lohse & Spiller, 1998:1).

Users want a “simple, clear interface” (Mayhew, 1999) which we posit will increase the perceived ease of use.

H2c: A simple and clear User Interface (UI) has a positive effect on Perceived ease of use (PEU).

109 The Impact of Social Exchange Related Factors on Viral Growth

Social Networking platforms are more than just a set of functional technologies

supporting tasks of a single user; they are also arenas that promote social exchanges

through co-creating that builds user-user dyads. Thus, they need to be understood as an

evolving service ecology that is jointly created by users and service providers (Jung &

Lyytinen, 2009). Yet, due to the individualistic and cognitive focus of traditional

technology acceptance models, drivers and determinants of social exchanges and features

of platform characteristics leading to retention and fan out have not been captured (or

explored??) Consequently, we integrate TAM and identify an alternative set of factors that act as a catalyst of viral growth. To provide a solid theoretical basis for selecting these factors, this paper integrates two important streams of literature: TAM, and theories of social exchange, and the literatures on service dominant logic (Lusch, Brown, &

Brunswick, 1992; Lusch & Vargo, 2006a, b; Vargo & Lusch, 2004; Vargo et al., 2008).

In conducting this study, we sought to understand how social and technological factors affect viral growth.

Social networking platforms center on sharing user generated content that form the nexus of social exchanges. Therefore, in social networking sites the platform provider

needs to foreground the content creation process enabled by the user-to-user dyad. This dyad, in principle, is driven by processes of co-creation and co-production. Here co- creation (CC) allows users to “actively co-construct their own consumption experiences through personalized interaction” (Prahalad & Ramaswamy, 2003:1) while co-production

(CP) is an activity in which users actively contribute and select various elements of a new digital service offering (O’Hern & Rindfleisch, 2009: 4). As shown in Figure C2, we

110 propose a social exchange model of viral growth, where increases in processes of co-

creation and co-production drives the viral growth – fan out and retention. Next we

discuss a social exchange model that explains viral growth in digital networking sites and

develop our hypotheses.

FIGURE C2: Social Exchange Model of Viral Growth in Digital Platforms

Antecedents to Fan out and Retention.

Co-creation. During co-creation, value is created with and determined by the user

during the ongoing generation and consumption of meaning and experience that is

associated with platform use (Lusch & Vargo, 2006b). Accordingly, services related to

social networking platforms such as Facebook, Twitter, or LinkedIn empower users to

interact instantly and constantly and co-create users experience that is valued during those interactions (Sigala, 2008). Thus, it is not surprising that co-creation has been

identified as an important process between the users when viral growth is present (Fisher

et al., 2011), and that the growth of user’s co-creation using social network sites are

invaluable to the growth of user related knowledge (Potts et al., 2008).

We emphasize that this happens outside the concerns for economic cost and is

primarily related to the user’s needs to identify, expand, and sustain social bonds. In a

111 sense, co-creation underlies each fleeting instance of user-content-user dyad offered on a

digital service platform. These social exchange behaviors also drive perceived user value,

which is primarily derived from the unique user experience generated during service

consumption, representing a radical departure from the co-valuation based logic on

economics based platforms. This also blurs the distinction between the value creator and

value consumer, or seller and buyer.

Motivations for the creation and use of content-related services form a different

set of factors than those used to explain participation in the exchange of economic goods.

That is, the latter process deals with maximizing utility and/or minimizing cost, whereas

former relates to those factors that influence why and how people participate in social

exchanges and social bonding. Some motivations are: 1) the need for self-validation, 2)

the desire to manage and project one’s self-identity, 3) the need to develop new and/or maintain established social relationships, 4) the desire to exert social control through persuasive communications, and 5) deploy indirect ways of monitoring other’s behaviors

(Calvert, 2004). The positive experiences a user derives from the act of co-creation itself

(e.g., having fun, feeling creative) can also explain the user’s increased intention to recommend the service to their friends, family, and colleagues (fan out) and return to use the service (retention) (Fisher et al., 2011). Accordingly we hypothesize:

H3a: Co-creation (CC) has a positive effect on fan out (FO).

H3b: Co-creation (CC) has a positive effect on retention (R).

Co-production. Through co-production customers become directly involved in the cycles of service design, production, and marketing based on their experiences during service process (Schultze & Bhappu, 2005). Customers can become involved in co-

112 production through service consumption (Lusch & Vargo, 2006a). Hilton and Hughes

(2008) suggest the key to success of service based organizations is to ensure clear

differentiation between the co-production and the co-creation processes. The degree of

co-production is a function of both the scope and the propensity of the service provider to

collaborate with customers across all phases of the service cycle, and the intensity and

extent to which the service provider relies on collaborations with users within a particular

stage (Hoyer et al., 2010). On social networking platforms, co-production is primarily

triggered by users who will ‘misuse’ or ‘improvise’ the service for purposes that were not

intended by the original designers14. Users can participate in co-production through their

participation in co-creation. Thus, the frequency and scale of co-creation is fundamental

to explaining the amount of co-production. We propose the following:

H3c: Co-creation (CC) has a positive effect on co-production (CP).

The decision of the user to engage in co-production is attributable to the perceived

benefits of creating new services that they believe are valuable. This is turn increases the

user’s motivation to continue using the service platform (retention). Simply stated, they

have more reasons to return to the site. It also has the potential to increase fan out as new

services are more likely to enroll untapped users through the invitation of existing users

(i.e. dog owners). We accordingly hypothesize:

H3d: Co-production (CP) has a positive effect on fan out (FO).

H3e: Co-production (CP) has a positive effect on retention (R).

14 One illustrative example is how Facebook learned from users starting to set up accounts for their pets. This later led to the 3rd party development of dogbook and catbook applications on Facebook resulting in over 1 million pet “users” and large amount of content related exchanges associated with pets by 2010. 113 Antecedents to co-creation and co-production: Exhibitionism and voyeurism.

The key issue in social exchange perspective is to address the question:

The social exchange theory provides a good explanation for the growth of a site.

Further, it can help us answer the question: What factors drive social exchanges? If it is not solely the economic utility that influences growth, what personal or social drivers will increase the scope and intensity of social exchanges on the site? We posit that factors that increase the distribution, scope and exchange of user related content, constitute the main drivers of co-creation and co-production. Moreover, we hypothesize the use of social networking platforms is driven by emotional and hedonistic factors (i.e. motivations of and ways in which the individuals relate to other individuals during these social exchanges).

Critical relationship-building blocks are how individuals project themselves within these social exchanges as part of their identity building process, and how they can observe and construct the identities and information of the others (Lee et al., 2001;

Sparks & Guthrie, 1998; Stryker, 1980, 1987; Stryker & Burke, 2000). To this end, we propose that an individual’s level of exhibitionism (E) and related processes of identity construction (Munar, 2010) are critical in influencing the generation of user related content. Social networking sites provide individuals many opportunities to continuously project their (oftentimes highly imagined) identities in the new brave dynamic and free floating digital world. Nakamura (2002) asserts that the physical body has become increasingly irrelevant in digitally mediated social exchanges, as individuals are free to construct their own likeness thereby becoming “entrepreneurs of the self.” The extent to which content exchanges on networking sites offer value for users is primarily

114 determined by whether the exchanges through digitally mediated capabilities lead users

to reveal and build new facets of “I” to a growing number of others who are known to

“watch”. Multiple salient functions for identity and social bonding are served by these

processes including: (1) self-clarification (2) social validation (3) relationship

development and (4) social control (Calvert, 2004). Users demonstrating exhibitionism

are more prone to upload pictures, post comments, and update statuses of their personal

information. By participating in the process of co-creation users hope that others will

view and respond to their stream of displays. As Jones (2010:262) notes, such

exhibitionism leads to content that “is inherently more authentic and thus more intimate

than producer-generated content.” Overall, the character and consumption of user-

generated content on the platforms is vastly different from the character and consumption

of traditional ‘producer’ centered content in traditional media. The latter follows closely

the economic co-valuation logic of multi-sided markets (Jones, 2010).

Revealing and constantly building facets of “I” to a growing number of others

allows “you” to see “I” in new ways. This increases opportunities for “others” (you) in

digital service platforms to “peep in” and examine “I” in new ways. When users access

content of others and engage in social exchanges, the use of social networking platforms

is driven by voyeuristic behaviors. Indeed, we found that higher levels voyeurism (V)

leads to higher levels of networking site use (Fisher et al., 2011). Voyeurism has traditionally been defined as a sexual disorder, or paraphilia, that involves observing unsuspecting individuals in sexual acts (American Psychiatric Association, 2000).

Recently, the term mediated voyeurism has been introduced to reflect the consumption of

“revealing images of and information about others’ apparently real and unguarded

115 lives…not always for purposes of entertainment, but frequently at the expense of privacy

and discourse, through the means of … Internet” (Calvert, 2004:2). People more inclined

to engage in voyeuristic behaviors are also more likely to participate in co-creation by

monitoring and viewing information about others. Digital ‘exhibitionism’ thus invites and

requires its opposite side of the coin – ‘digital voyeurism’. The increased ease of making

personal content available of individuals’ mundane daily experiences across social

networking platforms blurs the division between public and private space (Munar, 2010)

Moreover, this is changing the conventional code of what can or cannot be shown and to

whom (Koskela, 2002).

The dynamics of digitally mediated exhibitionism and voyeurism relies on and

spawns the expansion of user-generated content and the need for new services, thereby

increasing the scale of co-creation and co-production. We hypothesize:

H4a: Voyeurism (V) positively influences Co-creation (CC).

H4b: Exhibitionism (E) positively influences Co-creation (CC).

H4c: Voyeurism (V) positively influences Co-Production (CP).

H4d: Exhibitionism (E) positively influences Co-Production (CP).

Mediation of co-creation and co-production on fan out and retention. The

user’s ability to influence and shape the platform and to more fully express themselves,

can be seen as a critical determinant of whether users will recommend the use of the

platform to others. Hence, co-creation and co-production are hypothesized to directly affect fan out and retention. Co-creation facilitates the self-validation, management of one’s self-identity, and control through persuasive communications (Calvert, 2004) that

116 incentivizes users to recommend the service (fan out) and continue to return to use the service (retention).

In addition to direct effects, we hypothesize co-creation and co-production will mediate the effects of exhibitionism and voyeurism on fan out and retention. Increased levels of exhibitionism and voyeurism will drive fan out and retention when the platform features and capabilities enable higher levels of co-creation. Likewise, the tendency of individuals to exhibit voyeuristic and exhibitionistic behaviors will drive them to participate more in co-production, thereby increasing the levels of fan out and retention.

The dynamics of mediated exhibitionism and voyeurism requires the constant expansion of user-generated content and the satisfactory development of the platform to allow for these interactions. The ability to influence and shape platform services that allows users to fully express themselves is a critical determinant of whether they will recommend the platform to others. We hypothesize that the more individuals participate in voyeuristic and exhibitionistic behaviors, the more they will engage in co-production of the digital service, which in turn will be positively associated with fan out and retention.

H5a: Co-creation (CC) mediates partially and positively the effect of voyeurism (V) on fan out (FO).

H5b: Co-creation (CC) mediates partially and positively the effect of voyeurism (V) on retention (R).

H5c: Co-creation (CC) mediates partially and positively the effect of exhibitionism (E) on fan out (FO).

H5d: Co-creation (CC) mediates partially and positively the effect of exhibitionism (E) on retention (R).

H6a: Co-production (CP) mediates partially and positively the effect of voyeurism (V) on fan out (FO).

117 H6b: Co-production (CP) mediates partially and positively the effect of voyeurism (V) on retention (R).

H6c: Co-production (CP) mediates partially and positively the effect of exhibitionism (E) on fan out (FO).

H6d: Co-production (CP) mediates partially and positively the effect of exhibitionism (E) on retention (R).

The Combined Impact of Adoption and Social Exchange factors on Viral Growth

Typically, users do not engage in social exchanges without mindfulness to the technological factors of the platform nor do they determine their adoption and usage of a social networking platform based solely on increasing personal productivity. We posit that these two processes overlap and move concurrently. Our interpretation of the phenomenon suggests user’s behavior related to individual level technology acceptance and social processes of social exchanges are facilitated by the joint presence of co- creation and co-production, such that they influence the level of viral growth. Therefore, we argue that a combination of these factors will provide a stronger explanation for viral growth. As seen in Figure C3, we provide a final model of factors we believe influence viral growth. It integrates the two models proposed earlier in Figures 6 and 7 with new hypothesized causal effects (H7a-H9b).

118 FIGURE C3: An Integrated Model of Viral Growth

Effect of technology features on co-creation and co-production. The length and diction of the privacy policy (Pan & Zinkhan, 2006), along with implied compliance with standards of best practice for privacy have been shown to affect users’ attitudes towards online retailers (Miyazaki & Krishnamurthy, 2002). A factor expected to influence the extent to which users are willing to participate in co-production and co-creation is the privacy policy(ies) of the social network platform. The presence of a simple and comprehensible privacy policy will increase individual’s willingness to share content and

“experiment” with the functionality of the platform. We hypothesize:

H7a: The presence of a simple and comprehensible Privacy Policy (PP) will positively influence Co-creation (CC).

H7b: The presence of a simple and comprehensible Privacy Policy (PP)) will positively influence y Co-creation (CP).

119 Because it is our intent to explain continued use of social networking platforms

versus single user technologies indicative of an individual’s job performance, we expect

perceived usefulness to be an antecedent to users’ participation in the co-production and

co-creation. The direct effect on co-creation and co-production is driven by the user’s perceived usefulness of the functionality that underlies services of the digital platform for creating user generated content. If a user perceives the platform functions are useful in exchanging content about their personal or professional lives, they are more likely to engage in co-production and make it as useful as possible. Furthermore, they are more likely to participate in co-creation through the creation and consumption of content, if they perceive the platform functions are useful. Thus, we hypothesize:

H8a: Perceived Usefulness (PU) influences positively Co-creation (CC).

H8b: Perceived Usefulness (PU) influences positively Co-production (CP).

While we believe that single user level adoption factors are applicable and explain partially the viral growth processes, especially for continued use (retention), drivers related to social exchange (voyeurism and exhibitionism) – along with platform centric processes enabling social exchanges (co-creation and co-production) provide a stronger argument in explaining fan-out. Thus, we propose:

H9a: Co-production (CP) and Co-creation (CC) have a stronger influence on fan out (FO) than perceived usefulness (PU) and perceived ease of use (PEU).

H9b: Co-production (CP) and Co-creation (CC) have a weaker influence on retention (R) than perceived usefulness (PU) and perceived ease of use (PEU).

120 RESEARCH DESIGN AND METHODS

To empirically test the proposed model, we surveyed users of five of the 2010 top

social networking sites--- Facebook, Twitter, LinkedIn, MySpace, and Ning15 and three

social networks that had failed to achieve sustained viral growth in the United States (e.g.

Friendster, Yahoo! 360, Orkut) (see Appendix A). To this end we followed a

psychometric survey methodology that maps individual responses to the underlying

constructs within our model. Our model involved 11 constructs all of which were

measured with reflective scales.

Construct Definition and Operationalization

Dependent variables. The viral growth variable, retention, defined as the number

of users who return to use the service during a specific period of time (Jurvetson, 2000),

was operationalization using a scale from Vatanasombut, et al. (2008). To measure the

viral growth variable fan out, defined as the number of new users invited per existing user

(Penenberg, 2009). We borrowed items that Cheng and Chen (2007) developed for the

knowledge sharing of technologies related to health the number of new users invited

Independent variables and mediating variables. To measure page load time that

“the length of time between when a user selects a Web page and when the Web page is fully loaded and ready for consumer use,” we used items adapted from (Rose et al.,

2005). Consumers have significant privacy concerns about providing personal information online. Research found that the completeness of the privacy policy and the reputation of the company lower concerns while the offer of a reward heighten concerns

(Andrade et al., 2002). We utilized four items, adapted from Andrade, et al. (2002), to

15 According to http://www.ebizmba.com/articles/social-networking-websites 121 measure the presence of a simple and comprehensible privacy policy for the digital service platforms.

For the measurement of user interface, we adapted 10 items from the 27 item instrument developed by Chin et al. (1988) to measure user satisfaction on the human- computer interface, which involved organizational and political factors, the user support, and the pressures of a situation (Benyon, 1993).

The construct of voyeurism, which has its origin as a paraphilia in psychology

(American Psychiatric Association, 2000) has not been operationalized in the context of technology adoption nor in the context of service platforms. We adapted five items from

Kim and Rubin (1997) and three items from Chung (2008). To measure exhibitionism, we chose four items with the highest loading values on the Narcissistic Personality Index

(Kubarych et al., 2004)

We used items from Wixom and Todd (2005) to measure PEOU and PU. We adapted the items to focus on establishing and maintain relationships. To measure co- production, we utilized a three-item scale operationalized by Cheung and To (2010). Co- creation was measured using the four-item scale of Singh and Koshy’s (2010) Given the nested nature of co-production and co-creation (Hilton & Hughes, 2008), items were modified to better fit the digital service platform context.

Controls. In our model we controlled for users education, age, gender, and date they joined a social network. Age was quantified as the year the user was born. Education level was measured using an eight point scale ranging from “never graduated high school” to “post doctorate work”. We asked the user the year and month they joined each social network and their gender.

122 Measurement and instrument development. Items selected for the constructs were adapted from prior studies in order to ensure content validity. As these constructs had never been operationalized and used in the context of social networking platforms, care was taken to develop reliable and valid measures following the procedures suggested by DeVellis (2011).

The initial item pool consisted of 51 items that measured the 11 different constructs and 4 controls. We conducted several rounds of pretesting using concurrent verbal protocol content analysis (Bolton, 1993). As a result, four items were modified slightly to address problems with comprehension and judgment.

A pilot test with a sample of 35 respondents led to further refinement of the survey instrument based upon an assessment of reliability and validity. Five surveys were incomplete and removed from the analysis. We reviewed the items distributions, examined correlations using bivariate analysis, and conducted exploratory factor analysis

(EFA). Our scales demonstrated acceptable factor loadings ranging from 0.481 to 0.953, small cross loadings, and Cronbach alpha coefficients between 0.404 and 0.892. None of the respondents who participated in the pretest or the pilot test were included in the final sample. Appendix B contains the final survey and the items to construct mapping.

Sample

Two approaches were used to collect data. First, we leveraged the personal and professional network of the researchers by posting the link to the survey on the social networks being studied asking for participation and for assistance distributing the request by reposting to their networks. This “snowballing” technique is amenable to the same

123 scientific sampling procedures as ordinary sample (Coleman, 1958). Using this method

we received 432 responses.

Second, we distributed the survey via email to 229 undergraduate and 618

graduate students at the Weatherhead School of Management. We received 343

completed responses. To maximize response rates, we guaranteed anonymity, collected

no personally identifiable information, and assured respondents that only the researchers

would have access to the raw data.

The utilization of electronic distribution of surveys via online and email is widely

used as it offers researchers low cost, good response rates, and quick response times

(Sheehan & McMillan, 1999). Our response rate of 40.4% is well within the expected

response rates for email surveys which vary from a low of 6 percent (Tse, 1995) to a high of 75 percent (Kiesler & Sproull, 1986).

While the two modes of survey delivery used exactly the same survey, we wanted to ensure that two different sampling methods did not bias the results. An independent samples t-test indicated that the respondent groups differed significantly (p < 0.05) on

15/51 items. Of these 15 items, none comprised all items on any single construct, and were distributed as follows: 3 voyeurism, 3 exhibitionism, 1 page load time, 1 privacy policy, 3 user interface, and 4 on retention. The samples did not differ significantly for

gender, social networks participation, or the date they joined the networks. A significant difference between samples means was observed for year born (“snowball” sample =

1980; WSOM sample = 1983, t-value = 3.7, df=625, p < 0.001). Further, a confirmatory factor analysis (CFA) model tested with both samples was configurally invariant based on model fit (χ2 = 2283, df=720, p < 0.000, CMIN/DF=3.172, CFI=.941) and metric

124 invariant based on critical ratios (see Appendix C) across both samples. We elected to merge the respondents from both samples and treat them as a single group.

In total, we received 775 respondents, with a 14.1% dropout rate, categorized as such if more than 10% of the responses were missing. For those cases with less than 10% missing data we used the median of nearby points to transform the missing data. The remaining 666 respondents provided 1449 cases for analysis as respondents answered for multiple social networks. To test for unit non-response bias the time trend extrapolation procedure suggested by Armstrong and Overton (1977) was employed. The presumption in such a procedure is that respondents replying later to a survey are more likely to resemble non-respondents than early respondents, suggesting that significant differences between first and second administration respondents would predict differences between those who responded and those who did not. The results indicated that responses could be regarded as broadly representative of the pooled sample. Characteristics of the respondents are shown in Table C1 below.

125 TABLE C1: Demographics

Frequency Percent Gender Male 316 47.4% Female 338 50.8% Social Network Facebook 623 43.0% Friendster 30 2.1% LinkedIn 333 23.0% MySpace 170 11.7% Ning 14 1.0% Orkut 17 1.2% Twitter 242 16.7% Yahoo!360 20 1.4% Education Never Graduated High School 2 0.3% High School Graduate 28 4.2% Some College 212 31.8% Associate Degree 28 4.2% Bachelor Degree 163 24.5% Master Degree 195 29.3% Doctorate Degree 22 3.3% Post Doc 4 0.6% Adopter Early 336 50.5% Neither 269 40.4% Late 48 7.2% Age 18-24 290 45.9% 25-34 181 28.6% 35-44 104 16.5% 45-54 34 5.4% 55 and over 23 3.6%

Measurement Models

Before proceeding to the measurement model analysis, time was spent in

"cleaning" the data. Outliers may be removed by analyzing their z-score relative to a standard population, or by using formal outlier analysis such as the Dixon test (1950).

(Cohen 2003:128) suggest that “if outliers are few (less than 1% or 2% of n) and not very extreme, they are probably best left alone.” Because our questions were Likert scale responses and outliers were less than 1%, we did not delete any outliers. Since we intend to analyze the data with techniques that involve a normality assumption, it was necessary

126 to correct the problem of skewness for two items (Q6, Q9) by applying a square transformation. We analyzed Mahalanobis distance of data and removed the top one hundred data points farthest from the centroid. Since model fit did not improve they were not removed from the analysis. We also tested for heteroscedasticity of indicators and there was some threat to the model from several paths (see Appendix A). We tested finally for multicollinearity among indicators and did not find significant threat and did not remove any variables as all combinations had variance inflation factor (VIF) < 1.53.

As is the case with statistical significance testing in general, an assessment of model fit is confounded with sample size, because the power of the test increases, with increases in the sample size (Fan et al., 1999). Hu and Bentler (1998) show that goodness-of-fit statistics behave differently depending on the sample size. Thus, because of our large sample size we split the data randomly into two roughly even data sets (750 data points in set 1 and 699 data points in set 2). We used set 1 for the EFA and set 2 for the CFA and SEM.

Exploratory Factor Analysis

Accordingly, we begin our measurement model analysis first by conducting an

EFA and then a CFA. Because we were interested in identifying latent reflective constructs expected to produce scores on underlying measured variables (Fabrigar et al.,

1999; Tabachnick & Fidell, 2007) and interested in the shared variance (Costello &

Osborne, 2010), we used common factor analysis (Hair et al., 2010). Principle Axis

Factoring (PAF) technique was used as factors were assumed to be non-orthogonal

(correlated). We chose oblique rotation, because of its assumption of correlated variables.

Promax, an oblique rotation technique, and we applied the latent root criterion (factors

127 with eigenvalue less than 1.0 are not included) as well as the scree analysis to determine how many factors to retain.

The factorability of the 47 items was examined. Several well-recognized criteria for the factorability of a correlation were used. First, 100% of items correlated at least .30 or higher with at least one other item, suggesting reasonable factorability. Secondly, the

Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.909, above the

2 recommended value of .60, and Bartlett’s test of sphericity was significant (χ (435) =

26162.538, p < .000). The diagonals of the anti-image correlation matrix were all over

0.65, supporting the inclusion of each item in the factor analysis. Finally, the communalities were all above 0.30 further confirming that each item shared some common variance with other items.

While low inter-factor correlations allow the researcher to assume orthogonality and use orthogonal models like Varimax for analysis (Fabrigar et al., 1999), we observed a significant number of correlations greater than .30 (100%) suggesting nonorthogonality.

Thus, we continued our analysis with an oblique rotation using Principle Factor Analysis

(PFA). Moreover, our primary purpose was to test a theoretical model (Tabachnick &

Fidell, 2007). A promax rotation provided the best defined factor structure. The results showed an 11 factor solution with eigenvalues greater than 1.0 and the total variance explained was 64.622%. Two items (Q35 and Q34) were removed due to cross loading during EFA.

In the final model all factors loadings were above 0.5, demonstrating convergent validity (Bagozzi & Yi, 1988), while most of the factor loadings exceeded 0.7 which Hair et al. (2010) consider indicative of a well-defined factor structure. Two

128 motivations led the author to retain some items with factor loadings below 0.7. First from

a statistical perspective, it is recommended that researchers should design studies that

achieve a minimum statistical power level of 80 percent (Hair et al., 2010). Statistical

power is influenced by sample size. Specifically, a factor loading of 0.3 for a sample size

of 350 or greater achieves 80 percent statistical power (Hair et al., 2010:117). Therefore,

factor loadings below 0.3 were still capable of producing sufficient statistical power (>80

percent) given the large sample size (n=750). This leads to the second motivation for

retaining these items in the study. Hair et al. (2010:117) suggests that researchers “should realize that extremely high loadings (0.80 and above) are not typical and that the practical significance of the loadings is an important criterion.” In the case of this study, the items in question maintained practical significance to their respective constructs as well as

tapped into phenomenon under study. Table C2 below summarizes the results of the

measurement model (EFA and CFA).

129 TABLE C2: EFA & CFA Summary

Construct Item Mean Standard Loading Reliability Composite Average Maximum Average Deviation Coefficient Reliability Variance Shared Shared (Cronbach Extracted Variance Variance Alpha) Criteria > 0.50 > 0.70 > 0.70 > 0.50 < AVE < AVE Voyeurism Q4 2.30 1.144 .858 .863 .87 .77 .39 .07 Q5 2.09 1.089 .829 Exhibitionism Q6 1.54 .898 .623 .804 .81 .52 .43 .13 Q7 2.26 1.145 .606 Q9 1.78 1.000 .911 Q10 2.12 1.226 .646 Page Load Q11 3.84 .822 .729 .634 .69 .54 .24 .12 Q12 3.42 .997 .620 Privacy Q14 2.93 1.009 .665 .827 .83 .63 .16 .07 Policy Q15 3.04 .973 .855 Q16 3.11 .889 .826 User Q20 3.39 .985 .827 .806 .81 .68 .39 .21 Interface Q21 3.51 .894 .781 Perceived Q23 3.79 .926 .711 .890 .89 .68 .39 .17 Ease of Use Q24 3.70 .868 .633 Q27 3.82 .833 .931 Q28 3.83 .787 .922 Perceived Q29 3.62 1.031 .812 .805 .81 .67 .46 .21 Usefulness Q30 3.76 1.083 .645 Co- Q32 2.33 1.057 .504 .682 .68 .52 .44 .21 production Q35 2.64 1.255 .692 Co-creation Q34 3.36 1.063 .930 .830 .83 .71 .44 .22 Q36 3.23 1.092 .768 Retention Q39 3.70 1.214 .972 .930 .93 .72 .72 .25 Q40 3.64 1.041 .818 Q41 3.59 1.021 .785 Q42 3.49 1.149 .645 Q43 3.67 1.221 .985 Fan Out Q45 3.41 1.120 .587 .819 .83 .72 .72 .26 Q46 3.56 1.107 .567 Model Fit Statistics (CFA) Threshold Results Reference Chi Square (Deg of Freedom) 830.716 (347) Probability <0.05 0.000 Sample Size (n) > 5*items 699 Hair et al (2010) CMIN/DF < 2 2.321 Tabachnik & Fidel (2007) CFI > 0.95 0.967 Hu & Bentler (1999) PCFI > 0.5 0.771 Hu & Bentler (1999) RMSEA (LO 90 – HI 90) < 0.06 0.043 (.039-.047) Hu & Bentler (1999) PCLOSE > 0.5 0.999 SRMR < 0.09 0.0366 Hu & Bentler (1999)

Confirmatory Factor Analysis

We performed a CFA using the results from our EFA analysis. The sample size of

699 was deemed sufficient given the low communalities (Hair et al., 2010) and acceptable Hoelter's benchmark for Critical N which should exceed 200 (Nokelainen,

130 2009) (CN=482, p<.01). The fit of the CFA model was acceptable with sufficient

convergent and discriminate validity, (Appendix C).

During CFA we consulted modification indices to co-vary error terms within the

same construct – retention – specifically items Q26 with Q30, and Q27 with Q28. This

improved the model fit significantly. The theoretical basis for co-varying these terms is that Q27 and Q28 concern the ease of use of the social network. These items are closely related and therefore respondents are likely to answer them similarly. Likewise, items

Q26 and Q30 belonging to the retention construct, dealt with how quickly mistakes can be corrected and how quickly friends can be connected. Respondents are likely to have answered similarly to both, because of the temporal focus of the questions.

Confirmatory Factor Analysis: Reliability and Validity.

Fornell and Larcker (1981) listed three procedures to assess for convergent validity. These are item reliability of each measure, composite reliability of each construct, and the average variance extracted. Hair et al. (2010) suggested that an item is significant if its factor loading is greater than 0.50. As shown in Table C2, the factor

loadings of all the items in the measure range from 0.504 to 0.985, thus meeting the

threshold set by Hair et al., and demonstrating convergent validity at the item level. At

the construct level, Hair et al. recommended that the composite reliability should be used

in conjunction with SEM to address the tendency of the Cronbach’s alpha to understate

reliability. For composite reliability to be adequate, a value of .70 and higher was

recommended (Nunnally & Bernstein, 1994). The final indicator of convergent validity is

the average variance extracted, which measures the amount of variance captured by the

construct in relation to the amount of variance attributable to measurement error (Fornell

131 & Larcker, 1981). Convergent validity is judged to be adequate when average variance extracted equals or exceeds 0.50 (i.e. when the variance captured by the construct exceeds the variance due to measurement error). As shown in Table C2, the convergent validity for the proposed constructs of this study is adequate.

Fornell, Tellis, and Zinkhan (1982) state that discriminant validity is considered adequate when the variance shared between a construct and any other construct in the model is less than the variance that construct shares with its measures. The variance shared by any two constructs is obtained by squaring the correlation between the two constructs. The variance shared between a construct and its measures corresponds to average variance extracted. As such, discriminant validity was assessed by comparing the square root of the average variance extracted for a given construct with the correlations between that construct and all other constructs. Table C3 shows the correlation matrix for the constructs. The diagonal elements have been replaced by the square roots of the average variance extracted. For discriminant validity to be judged adequate, these diagonal elements should be greater than the off-diagonal elements in the corresponding rows and columns. Discriminant validity appears satisfactory for all constructs. This indicates that each construct shared more variance with its items than it does with other constructs. Having achieved discriminant validity at both the item and construct levels, the constructs in the proposed research model are deemed to be adequate.

132 TABLE C3: Correlation Matrix*

PU FO R CC CP PEU UI PP PLT E V

Perceived PU .819 Usefulness

Fan Out FO .631 .849

Retention R .709 .806 .849

Co-create CC .507 .532 .446 .843

Co- CP .274 .490 .233 .699 .721 production

Perceived PEU .584 .357 .531 .376 .067 .825 Ease of Use

User UI .514 .476 .578 .386 .286 .676 .825 Interface

Privacy PP .009 .079 .132 .154 .229 .145 .387 .794 Policy

Page Load PLT .156 .158 .398 .029 -.284 .258 .353 -.019 .735 Time

Exhibitionism E -.023 .057 -.163 .211 .540 -.218 -.159 -.160 -.454 .721

Voyeurism V .035 -.011 -.156 .038 .324 -.133 -.104 -.170 -.331 .678 .877

* Sqrt(AVE) on diagonal

Common Method Bias

Several post hoc tests determined the extent to which common method bias was present in our data. First, using Harman’s single-factor test, all items were entered into an unrotated principal components factor analysis to determine the number of factors necessary to account for the variance in the variables. Accordingly, if a single factor emerged or a single general factor explained most of the variance between the independent and dependent variables, common method variance might be present

(Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). We found that a single factor

133 explained 29.5% of the variance. These results provide initial evidence that response bias is not a problem in the data (Podsakoff & Organ, 1986).

Second, we used the CFA-based Harman’s single-factor test in which we hypothesized a single common methods bias (CMB) factor as causing all the indicators.

We ran the model using a smaller sample size of 300 which is “subject to more strict evaluation” (Hair et al., 2010:654) than larger samples or more complex models. The

CMB factor extracted 5.4% of the variance. A χ2 difference (χ2= 2.371;df=1, p=0.124)

test between the baseline with all the CMB paths free floating, and the CMB with all

paths equal to zero, was not significant suggesting that CMV does not appear to be a

source of variance in the observed items. A summary of variance estimates are provided

in Appendix C.

Structural Model

In this research we adopted structural equation modeling (SEM) for data analysis

to study the causality between model parameters. The estimation of parameters used

maximum likelihood estimation (MLE). A separate structural equation model was

specified in AMOS to test the validity of each of the three hypothesized models, referred

to in Table C4 as modified TAM, social exchange, and combined model, respectively.

We ran these three separate models to detect changes in the path coefficients and

R squares, when new antecedents were being added to explain the viral growth. The final

trimmed model based on the combined model was then used for reporting hypothesis

tests i.e. the hypotheses were validated in the presence of all hypothesized paths. The

final trimmed combined model consisting of the modified TAM and social exchange

models is depicted in Figure C4. The model was revised by removing insignificant paths,

134 adjusting modification indices and adding covariance paths when there was a theoretical justification (Hair et al., 2010). After bootstrapping, where we tested for the presence of mediation, all three models were trimmed to generate the most parsimonious model with good fit statistics. As seen in Table C4, the fit statistics for all three models were acceptable (Hu & Bentler, 1999). Tabachnick and Fidell (2007) recommend a CMIN/DF less than 2 which both the partial models – modified TAM and social exchange model - achieved while the combined model had a slightly higher value. The combined model’s goodness of fit measurements were good: CMIN/DF = 2.068, CFI = .994, SRMR =

.0218, RMSEA = .039 (LO = .025, HI = .053), PCLOSE = 0.899.

FIGURE C4: Final Structural Model Direct Effects

135 We performed a mediation analysis using causal and intervening variable

methodology (Baron & Kenny, 1986a; MacKinnon, Lockwood, Hoffman, West, &

Sheets, 2002) and followed techniques described by Mathieu and Taylor (2006).

Mediated paths connecting independent variables to dependent variables through a

mediating variable were analyzed to examine the presence of direct, indirect, and total

effects. Causal steps approach (Baron & Kenny, 1986) (tests for significance of different

paths) and difference in coefficients and chi-squared test were utilized in mediation tests.

A bootstrapping method with 2000 re-samples with 95% CI was used for testing significance of the indirect path coefficients. We also used the results of the bootstrapping technique to determine the nature of the mediation.

TABLE C4: Model Fit Statistics for Structural Analysis

Model Fit Statistics Threshold Modified TAM Social Exchange Combined Model Reference Model Chi Square (DF) 0.072 (1) 0.672(1) 62.034 (30) Probability <0.05 0.788 .412 .001 Sample Size (n) > 5*(items) 699 699 699 Hair et al (2010) CMIN/DF < 2 0.072 0.672 2.068 Tabachnick & Fidel (2007) CFI > 0.95 1.000 1.000 0.994 Hu & Bentler (1999) PCFI > 0.5 0.167 0.036 0.328 Hu & Bentler (1999) RMSEA (LO 90 – HI 90) < 0.06 0.000 (.000-.065) 0.000 (.000-.090) 0.039 (.025-.053) Hu & Bentler (1999) PCLOSE > 0.5 0.910 0.723 0.899 SRMR < 0.09 0.0012 0.0038 0.0218 Hu & Bentler (1999)

FINDINGS

A summary of the structural model test results for the combined model is shown

in Table C5. We found strong support for H1a. Perceived usefulness had a positive

effect on fan out (β= 0.48, p<0.001. A positive and significant effect of perceived usefulness on retention (β= 0.55, p<0.001) provided support for H1b.

136 The hypothesized effect of perceived ease of use on fan out was not significant,

thus H1c was not supported. However, we did find a significant, positive relationship

between perceived ease of use and retention (β= 0.11, p <0.001).

The hypothesized effect of privacy policy on perceived usefulness (β= -0.15, p<

0.001) did not provide support for H2a. Similarly, we observed no support for H2b, the

hypothesized effect of page load time on perceived usefulness (β= -0.039, p < 0.251).

The results confirm support for H2c. User interface has a very strong, significant, and

positive effect on perceived ease of use (β= 0.72, p<0.001).

Co-creation did not have a significant impact on fan out or retention (H3a and

H3b not supported). We observed that the direct effects of co-creation on fan out and on

retention both dropped from significance when the pathway from co-creation to co-

production was added in the model. The data show that co-creation was significantly and

positively related to co-production (β= 0.60, p< 0.001), fan out (β= 0.44, p<0.001) and

retention (β= 0.14, p<0.001). Therefore, H3c, H3d and H3e are supported.

We did not find support for H4a, the hypothesized effect of voyeurism on co-

creation (β= -0.22, p<0.001). We found support for H4b. Exhibitionism had a significant and positive effect on co-creation (β= 0.44, p<0.001). We did not find support for H4c.

The hypothesized effect of voyeurism on co-production (β= .04, p<.069) was not

significant. The results did support H4d. The effect of exhibitionism on co-production

was positive and significant (β= 0.34, p<0.001).

We did not find support for H5a, the effect of voyeurism on fan out being

positively mediated by co-creation, and H5b, the hypothesized effect of voyeurism on

retention being positively mediated by co-creation, (indirect β= -0.07, p<0.001) (indirect

137 β= -0.02, p<0.001) respectively. We found support for H5c, the hypothesized effect of

exhibitionism on fan out mediated by co-creation, and H5d, the hypothesized effect of

exhibitionism on retention mediated by co-creation (indirect β= 0.13, p<0.001) (indirect

β= 0.04, p<0.001) respectively.

We did not find significant support for H6a, the hypothesized positive effect of

voyeurism on fan out mediated by co-production, nor H6b, the hypothesized effect of voyeurism on retention mediated by co-production (indirect β=0.015, p<0.073) (indirect

β= 0.007, p<0.070), respectively. We found support for H6c, the hypothesized effect of exhibitionism on fan out mediated by co-production, and H6d, the hypothesized effect of exhibitionism on retention mediated by co-production (indirect β= 0.19, p<0.001)

(indirect β=0 .09, p<0.001) respectively.

We found support for H7a and H7b. Privacy policy exhibited a strong positive effect on co-creation (β= 0.16, p<0.001) and co-production (β= 0.11, p<0.001). The results did support H8a (β= 0.43, p<0.001), there is a significant effect of perceived usefulness on co-creation, but not H8b (β= 0.001, p<0.978), the hypothesized direct

effect of perceived usefulness on co-production.

We found support for H9a, that the effect of co-creation and co-production on

fan out is equal or greater than the effect of perceived usefulness and perceived ease of

use on fan out. The direct paths to fan out from both co-creation and perceived ease of

use were trimmed. The remaining paths to fan out from co-production and perceived

usefulness had the same beta coefficients (β=0 .41, p<0.001) and strong explanatory

power on fan out (R2= 0.56).

138 We found support for H9b. The effect of co-creation and co-production on retention is equal or weaker than the effect of perceived usefulness and perceived ease of use on retention. The path from co-creation to retention was trimmed. The remaining paths to retention were significant with beta coefficients from co-production (β= 0.14, p<0.001), perceived usefulness (β= .56, p<0.001), and perceived ease of use (β= 0.10, p<0.001), with strong explanatory power on retention (R2= 0.65).

TABLE C5: Hypotheses Summary

Partial Models Combined Model Beta Beta Direct w/o Med Support / Direct w/o Med Support / Hypothesis Direct w/ Med Mediation Direct w/ Med Mediation Indirect Indirect H1a: PU  FO 0.62*** Yes 0.49*** Yes H1b: PU  R 0.59*** Yes 0.56*** Yes H1c: PEU  FO Ns No ns No H1d: PEU  R 0.20*** Yes 0.10*** Yes H2a: PP  PU -0.17*** No -0.15*** No H2b: PLT  PU Ns No ns No H2c: UI  PEU 0.72*** Yes 0.72*** Yes H3a: CC  FO 0.300*** Yes ns No H3b: CC  R 0.365*** Yes ns No H3c: CC  CP 0.55*** Yes 0.60*** Yes H3d: CP  FO 0.264*** Yes 0.41*** Yes H3e: CP  R Ns No 0.14*** Yes H4a: V  CC -0.23*** No -0.22*** No H4b: E  CC 0.45*** Yes 0.44*** Yes H4c: V  CP Ns No ns No H4d: E  CP 0.39*** Yes 0.34*** Yes H5a: V  CC  FO -0.097(ns) 0.046(ns) -0.115*** Indirect -0.111*** -0.055* -0.068*** Partial H5b: V  CC  R -0.090(ns) 0.047(ns) -0.099*** Indirect -0.069** -0.059** -0.022*** Partial H5c: E  CC  FO 0.132* -0.290*** 0.217*** Partial 0.137*** -0.169*** 0.128*** Partial H5d: E  CC  R -0.108* -0.354*** 0.187*** Partial 0.062** -0.055* 0.041*** Partial H6a: V  CP  FO -0.097(ns) 0.047(ns) 0.013(ns) None -0.111*** -0.058* 0.015(ns) None H6b: V  CP  R -0.090(ns) 0.047(ns) 0.004(ns) None -0.069** -0.060*** 0.007(ns) None H6c: E  CP  FO 0.132* -0.298*** 0.163*** Partial 0.137*** -0.177*** 0.191*** Partial H6d: E  CP  R -0.108* -0.348*** 0.053*** Partial 0.062** -0.057* 0.092*** Partial H7a: PP  CC 0.16*** Yes H7b: PP  CP 0.11*** Yes H8a: PU  CC 0.43*** Yes H8b: PU  CP ns No H9a: CC & CP  FO equal or CC (ns) Yes – equal stronger than PU & PEU CP 0.41*** Fan Out PU 0.41*** (R2 = 0.55) PEU (ns) H9b: CC & CP  R equal or CC (ns) Yes – weaker weaker than PU & PEU CP 0.14*** Retention PU 0.56*** (R2 = 0.64) PEU 0.10***

139 Post Hoc Analysis

In the fully trimmed combined model we did not find support for any of the H5 hypotheses - as the paths from co-creation to fan out and retention were trimmed.

Overall, the trimmed model produced four mediated chains in the paths voyeurism & exhibitionism co-creation  co-production  fan out & retention.

We performed a post hoc analysis using Shrout and Bolger’s (2002) and

Fletcher’s (2006) tests to determine if the paths were statistically significant. These tests do not assume that the chosen predictor significantly impacts the dependent variable alone, but rather it uses bootstrap and bias corrected estimates to test for the significance of the mediation effect, which yields greater power (Shrout & Bolger, 2002). This procedure allows us to distinguish among three types of mediated effects: indirect, partial, and complete. Mediated effects (partial / complete) describe the nature of the relationships that exist between X  Y when the direct effect C is significant. By contrast, an indirect effect assumes that the direct effect C is non-significant. See Table

C6 for the detailed results of the analysis. Figure C5 shows the results of the mediation analysis.

FIGURE C5: The Influence of Voyeurism on Fan Out

140 We observed that Voyeurism has no direct effect on fan out (β= -0.07, p<0.153)

nor retention (β= -0.05, p<0.222) but we did find support for the indirect effect or distal

mediation on fan out through co-creation and co-production (Figure C6) with the indirect effect path (abc+af+ec) as significant but negative (β= -0.07, p<0.010). The distal impact

of voyeurism on retention was positive and approached significance (β=0.06, p<0.08).

This post hoc analysis revealed that voyeurism has a negative effect on fan out

when distally mediated through the chain of co-creation and co-production. This implies

that even when the user participates in co-creation of content (by consuming it) and co- production of services (by consuming the misused features), the negative perception of participating in voyeurism prevents users from wanting to share the experience with others.

FIGURE C6: The Influence of Exhibitionism on Fan Out and Retention

As shown in Figure C6, exhibitionism has a direct effect on fan out (C path β =

0.18, p < 0.002) that remains significant with the addition of the mediator (c’ path β= -

141 0.21, p < 0.001) suggesting a partial mediation chain through co-creation and co- production with the total mediated effect path significant (β= -.21, p<.001).

Exhibitionism has a direct effect on retention (β=0.09, p < 0.046) that becomes non- significant with addition of the mediator (c’ path β= -0.02, p<0.722) suggesting a complete mediation chain through co-creation and co-production with the total mediated effect path significant (β= 0.11, p<0.011).

This post hoc analysis reveals that exhibitionism is partially mediated by the chain through co-creation and co-production on fan out and is completely mediated by the chain through co-creation and co-production on retention. This implies that users participating in exhibitionistic behaviors will fan out to others regardless of the presence of the platform processes of co-creation and co-production, likely so because the desire to have others view or comment on their exhibitionistic behaviors provides some level of satisfaction. If these platform processes are present, exhibitionism will not only drive greater fan out, but will facilitate continued and greater usage of the service, retention.

142 TABLE C6: Indirect Effects of Voyeurism and Exhibitionism on Fan Out and Retention

Bootstrap Mediation Path Path Stand Est. p-value Voyeurism  Co-creation C VFO w/o med -.066 .153  Co-production  Fan a VCC -.227 .001 Out b CCCP .560 .001 (distal mediation) c CP FO .398 .001 e V CP .059 .023 f CC  FO .286 .001 c' V  FO w/ med .002 .993 abc+af+ec V  FO -.069 .010 Voyeurism  Co-creation C VR w/o med -.054 .222  Co-production  a VCC -.227 .001 Exhibitionism b CCCP .560 .001 (no mediation) c CP R .178 .001 e V CP .059 .023 f CC  R -.016 .721 c' V  R w/ med -.110 .001 abc+af+ec V  R .056 .077 Exhibitionism  Co-creation C EFO w/o med .181 .002  Co-production  Fan a ECC .446 .001 Out b CCCP .560 .001 (partial mediation) c CP FO .398 .001 e E CP .414 .001 f CC  FO .286 .001 c' E  FO w/ med -.210 .001 abc+af+ec E  FO .391 .001 Exhibitionism  Co-creation C ER w/o med .094 .046  Co-production  a ECC .446 .001 Retention b CCCP .560 .001 (complete mediation) c CP R .178 .001 e E CP .414 .001 f CC  R -.016 .721 c' E  R w/ med -.014 .722 abc+af+ec E  R .108 .011

Model Comparison

All of the H1 and H2 hypotheses (H1a,b,c,d & H2a,b,c) were supported or unsupported in a similar manner between the modified TAM and combined model.

Between the social exchange model and the combined model three hypotheses differed as shown in Table C7. H3a and H3b, which hypothesized the direct effect of co-creation on 143 fan out and retention were supported in the social exchange model, but not in the

combined model. H3e, the hypothesized direct effect of co-production on retention was

not supported in the social exchange model, but was supported in the combined model.

The likely reason that co-creation lost explanatory power on fan out and retention in the combined model is inclusion of perceived usefulness provided much greater explanatory power for fan out (β=0.49, p<0.001) and retention (β=0.56, p<0.001). From a practical standpoint, a user engaging in social exchange is more likely to fan out and possibly return, if they perceive value or usefulness in the social network platform, or if they are able to misuse the service – co-production.

The likely reason that the effect of co-production on retention was significant in the combined model was that once co-creation lost significance and was trimmed, the explanatory power of co-creation on retention was mediated through co-production. This implies that the explanation for a user engaged in the social exchange of content returning to the social network platform is attributed by their ability to participate in the co- production process through co-creation.

TABLE C7: Summary of Model Differences

Partial Models Combined Model Hypothesis Beta Support Beta Support H3a: CC  FO 0.300*** Yes Ns No H3b: CC  R 0.365*** Yes Ns No H3d: CP  R Ns No 0.14*** Yes

As hypothesized, voyeurism and exhibitionism, along with platform centric

processes, co-creation and co-production, have equal explanatory power to fan out as

perceived usefulness and perceived ease of use.

144 Our data demonstrated that our modified TAM model resulted in very similar explanatory power of 42% and 56%ccompared to the TAM3 results reported by

Venkatesh and Bala (2008) of between 40-53% of the variance in behavioral intention across the different time periods. Our combined model demonstrated greater explanatory power, 56% and 65% explained variance for the two viral growth factors of fan out and retention, demonstrating a 16% and 33% improvement over the modified TAM model alone. Table C8 provides comparisons of our social exchange and combined models to our modified TAM model. It appears we lose explanatory power with the social exchange model when compared to the modified TAM model, but we gain a significant amount of explanatory power with the combined model.

TABLE C8: Model Comparison

R2 Improvement to TAM Model Fan Out Retention Fan Out Retention Modified TAM 0.42 0.56 Social Exchange 0.30 0.30 -29% -46% Combined 0.56 0.65 31% 14%

We controlled for confounding factors – gender, year born, date of joining the social network, and education level. As shown in Table C9, the date that the user joined the social network has a significant negative effect on both fan out and retention suggesting that over time there is less use and the user is less likely to recommend the system to his or her peers. The education level had a significant positive effect on retention.

145 TABLE C9: Control Effects

Beta Fan Out Retention Gender 0.028(ns) -0.005(ns) Year Born -0.077** -0.022(ns) Join Date -0.082** -0.098*** Education 0.030(ns) 0.087***

DISCUSSION

We set out to research objective was to identify factors that affect the level of viral growth, measured as fan out and retention, in social networking platforms. We posited 30 hypotheses about individual adoption and social exchange behaviors. Overall, the predictive and explanatory power of our combined model was found to be excellent as only four of 30 hypothesized relationships were not supported in the combined model and the amount of variance explained by the model was high (R2=0.65). Additionally, we

provide statistical evidence that the explanatory power for fan out and retention was

much stronger with the combined model than the modified TAM, suggesting that

individual adoption factors alone do not adequately explain the growth and continued use

of the social networking platforms. We will review these findings next in more detail.

Perceived Ease of Use and Fan Out

In both the modified TAM and the combined model, perceived ease of was

observed as insignificant in affecting fan out. One possible explanation is that the

perceived ease of use might only impact fan out for new and recent users. This finding is

confirmed by Venkatesh and Bala (2008) who found that when they divided their

respondents by time period, perceived ease of use was not significant at the later time

period, suggesting a moderating effect of experience in the relationship between

146 perceived ease of use and behavioral intention. Additionally, it may be that perceived

ease of use does not affect the actual use, but perceptions of whether one would use the service. Our results indicate ease of use does not affect whether others are invited, only if it is perceived to be useful.

Page Load Time

The page load time was not significantly related to perceived usefulness in either the TAM or combined models. Prior research has shown that even slight delays in the download of web pages have significantly reduced Internet searches (Brutlag, 2009), ecommerce site browsing (Galletta, 2002), and corporate website success in establishing

community and loyalty (Palmer, 2003). Our results concur with prior research, page load

time was a significant direct predictor of fan out (β=0 .16, p<0.001) and retention (β=

0.26, p<0.001). This suggests that page load time is not viewed by users as an antecedent

for perceived usefulness, but that it does directly affect whether users continue to use or

recommend the service.

Co-production and Retention

In our social exchange model, co-production, the process by which new features

are created, did not affect retention. If a feature does not already exist, some users are not

likely to continue using the service until that feature is built. Thus, the rapid development

of features based on co-production is very important for social networking platforms. In

the combined model, we did find co-production to directly affect retention. Post hoc

analysis found support for a chained mediation from exhibitionism through co-creation

and co-production to retention. Service dominant logic models have typically predicted

that co-production is a component of co-creation suggesting a formative relationship

147 (Hilton & Hughes, 2008; Lusch & Vargo, 2006a). In contrast, our finding suggests that co-creation is a significant antecedent to co-production in social networking platforms.

This makes sense in our study’s particular context in that the co-creation of new content through the ‘misuse’ of services is the means by which co-production on digital platforms is most often carried out.

Voyeurism on Fan Out and Retention

In both the social exchange model and the combined model, co-production alone did not mediate the relationship between voyeurism and retention or fan out. On social networking platforms, co-production occurs when users misuse features for some purpose other than originally intended. Examples of this on Facebook include the misuse of accounts for classes and groups for parties as Chris Cox, VP of Product at Facebook, stated at the f8 conference in 2010: “watch(ing) users misuse what we had already given them and build(ing) the product that captured what they want to do” Cox (2010).

Students used the profile feature to “friend” fraternities and classes; but, because it was not logical to “friend” a class, Facebook built the “groups” feature. This feature, in addition to being used for clubs, classes and teams, was used for parties. But parties, unlike fraternities, needed start times, consequently Facebook built "my parties" which evolved into its “events” feature.

Because individuals involved primarily in voyeuristic behaviors view other users’ personal information and pictures, there is little opportunity to misuse a product feature.

Individuals involved primarily in exhibitionistic behavior, however, are motivated to misuse service features to show off some aspect of themselves or their environment such as a pet, or that they are hosting a party.

148 Voyeurism was also found to have a negative effect on fan out and retention both

directly and indirectly through co-creation. Additionally, the post hoc analysis suggested

that voyeurism had a negative effect on fan out when distally mediated through co- creation and co-production. These findings suggest that users who primarily engage in voyeuristic behaviors do not drive fan out: they only use the platform for examining the

others. In recent research, the term mediated voyeurism has been used to describe the

consumption of images and information of other peoples’ supposed real lives for

entertainment, but at the expense of privacy (Calvert, 2004:2) which challenges the

traditional divisions between the public and the private personal content (Munar, 2010)

and changes the conventional code of what can or cannot be shown and to whom

(Koskela, 2002). Our research extends this dialogue of voyeurism on social networking

platforms by suggesting that the behavior negatively affects the growth factor of fan out,

implying that users participating in such behaviors desire to shield this fact from others.

In contrast, users who are mostly exhibitionistic drive fan out and thus are critical for viral growth, as they want others to see what they do. Similarly, retention is not driven by voyeurism – voyeuristic users utilize the service to see what happens once but the likelihood of returning for these reasons declines. Voyeurism is primarily about the consumption of content and, as noted, the creation of this content appears to be motivated by exhibitionism, which is enabled by co-creation and co-production, which result in the increased fan out and retention.

Exhibitionism

In the social exchange model, exhibitionism had a negative direct effect on retention and a positive direct effect on fan out. In the combined models, exhibitionism

149 was found to have a positive direct effect on both retention and fan out and partially mediated positively through co-creation and co-production. In the post hoc analysis, exhibitionism was shown to have a positive distally mediated effect on fan out and retention chained through co-creation and co-production.

A possible explanation for the negative direct effect of exhibitionism on retention

in the social exchange model is that exhibitionism is seen as a negative personal trait in

most social settings and cultures, but when facilitated through the “hidden” services of a

digital platform, the process of co-creation, can be disguised to make it social acceptable.

Additionally, users who desire to practice exhibitionistic behaviors that the service

prohibits will not be motivated to return. However, if the service allows for the misuse of

services through co-production, e.g. users misusing “friends” and “groups”, these users

would want to continue using the platform services.

Prior research has suggested that co-creation of content facilitates the self-

validation, management of one’s self-identity, and control through persuasive communications (Calvert, 2004). Our findings support this research and build upon it by demonstrating the distal mediation of exhibitionism on retention and fan out is chained through both co-creation and co-production.

CONCLUSIONS

This study is the first to investigate the role of individual behaviors (voyeurism and exhibitionism) and co-creation and co-production processes on social networking platform growth through the process of viral growth (fan out and retention). We argue that the antecedents of viral growth (fan out and retention) on social networking platforms are more than just single user level adoption factors and must involve

150 individual behaviors (voyeurism and exhibitionism) and social exchange processes on

digital service platforms (co-creation and co-production).

As social networking platforms continue to develop through the constant

amalgamation of components and form a service ecology, the explanatory power of

models explaining platform growth need to recognize effects derived from collective use

processes (Lyytinen, 2010; Valente, 1996). Additionally, current state-of-the-art research

attempts to explain the phenomenon of viral growth through an economic (Van den

Bulte, 2010; Van den Bulte & Lilien, 2001; Van den Bulte & Stremersch, 2004) or

network based lens (Abrahamson & Rosenkopf, 1997; Enders et al., 2008; Hamm, 2008)

have remained insufficient to explain growth in social networking sites. These research

streams fall short in that their focus is on economic exchanges of value or on structural drivers of growth – neither of which explain individual or social antecedents which could drive this growth. This study makes an important theoretical contribution toward articulating the impact of these antecedents on the determinants of fan out and retention.

We provide evidence that individual behaviors and platform processes as antecedents provide greater explanatory benefits to the viral growth determinants of fan out and retention, than one of the most widely utilized, single user technology adoption theories,

TAM3. Additionally, the antecedents of perceived ease of use and voyeurism have insignificant effects on fan out while the antecedents of perceived usefulness and exhibitionism positively affect both fan out and retention. These results represent an important first step toward a deeper understanding of the antecedents of viral growth on social networking platforms. Our contributions to theory include demonstration that the combined social exchange and TAM models provide greater explanatory power to

151 explain individual and social elements that shape viral growth. We provide evidence that suggests fan out is not affected by the perceived ease of use of the digital service platform, and is negatively affected by voyeurism both directly and mediated. However, fan out and retention are both positively affected by exhibitionism when facilitated through co-creation and co-production processes of the digital service platform. Lastly, our results indicate that the technological characteristic of page load time directly affects fan out and retention.

The study offers several lessons for managers. First, social networking platforms should strive to promote co-creation and co-production processes. While voyeurism has either no or a negative effect on fan out and retention, exhibitionism has a positive effect on fan out when mediated by these processes. Users should be encouraged to co-create value through content creation and co-produce the digital service platform through misuse of services. Technical characteristics of the platform, such as page load time, should be monitored to ensure users are satisfied with the performance thereby leading to further fan out and retention.

This study is not without limitations. First, this study focused on social networking platforms and did not include other types of service platforms such as ecommerce (Amazon), auction (E-bay), or content providers (e.g. Yahoo). While we expect that some of our findings may be generalizable, it is likely some of our findings are specific to social networking platforms. Second, when we operationalized the user behavior constructs of voyeurism and exhibitionism we adopted items from domains that are quite different from our study. We attempted to limit respondents’ cognitive difficulties by conducting several rounds of pretesting using Bolton’s (1993) techniques,

152 but the possibility for confusion remains. Additionally, individuals may have been

reluctant to self-report voyeuristic or exhibitionistic behaviors, even in an anonymous survey, due to the social stigma associated with these behaviors.

Finally, our findings suggest several avenues for future research. First, more

research is needed to explore other social identity based user behaviors, such as

establishing relationships and entertainment that are likely additional antecedents to the

digital service platform processes of co-creation and co-production. Second, research should focus on the differences between individual social networking digital service

platforms as it pertains to co-creation and co-production in order to explore the

differences among social networking sites. Third, while this t study focused on social

networking platforms, a broader more inclusive inquiry involving other digital platforms,

such as auction, content, and ecommerce sites, should be undertaken to increase the

generalizability of our model. Fourth, this study could be extended by exploring the

temporal impact of social exchange processes over time.

153 Sub-Appendix A

Alexa Ranking

Social Alexa Ranking Network (May 2010) Facebook 2 Friendster 987 LinkedIn 16 MySpace 84 Ning 261 Orkut 115 Twitter 9 Yahoo!360 3000 YouTube 3 Flickr 32 ShareThis 2392 Evernote 1719 Yelp 343

154 Research Questionnaire

C1 Informed Consent Form Introduction: Thank you for agreeing to participate in a research study conducted by Michael Fisher, a doctoral candidate at Case Western Reserve University. The purpose of this research is to best understand the use of social networking platforms.

Time Commitment: The amount of time required for your participation will be 10-15 minutes. You are asked to complete the survey to the best of your ability.

Risks: There are no major risks associated with this research. Nothing asked is personal in nature. Be assured that we will not share any of this information with anyone.

Privacy: The records of this research will be kept private. In any report the researchers publish, the researchers will not include any information that will identify a participant. Any information you provide will be kept in a secure password protected file and firewall protected from internet access. No one will ever know whether or not you were selected for this study, whether or not you participated, and how you responded or did not respond to the study questions. No printed information will be discarded—all printed documents will be shredded.

Access to information will be limited to the researchers, the University Review Board responsible for protecting human participants, and regulatory agencies. Further, no identifying information will be included in the research findings. Participation Your participation in this research study is voluntary. You may choose not to participate and you may withdraw your consent to participate at any time. If you choose not to participate, it will not affect your current or future relations with Case Western Reserve University. You will not be penalized in any way should you decide not to participate or to withdraw from this study.

Questions: The researchers conducting this study are Dr. Kalle Lyytinen and Michael Fisher. If you have any questions, you may contact Dr. Lyytinen at [email protected] or Michael Fisher at [email protected]. If the researchers cannot be reached, or if you would like to talk to someone other than the researcher(s) about; (1) concerns regarding this study, (2) research participant rights, (3) research-related injuries, or (4) other human subjects issues, please contact Case Western Reserve University's Institutional Review Board at (216) 368-6925 or write:

Case Western Reserve University Institutional Review Board 10900 Euclid Ave. Cleveland, OH 44106-7230

Thank you for your time and support.

C2 I have read and understood the above consent form and desire of my own free will to participate in this study.  Yes (1)  No (2) If No Is Selected, Then Skip To End of Survey

SN Please select which of the following social networks you have in the past or do currently use.  Facebook (1)  Friendster (2)  LinkedIn (3)  MySpace (4)  Ning (5)  Orkut (6)  Twitter (7)  Yahoo!360 (8) If QID4 (Count) Is Equal to 0, Then Skip To End of Survey

Instr1 For the questions on the following five pages, please indicate to what extent you agree (or disagree) with the statements about each of the social networking sites that you have in the past or currently do use.

155 Q1 1) I participate in this social network to learn how to do things I haven't done before. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q2 2) I participate in this social network to learn things about myself and others. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q3 3) I participate in this social network because it has the personal information of others. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

156 Q4 4) I participate in this social network because it shows the secret activities of others. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q5 5) I participate in this social network because it tells others’ secrets. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q6 6) I find participating in this social network to be sexually arousing. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

157 Q7 7) Participating in this social network allows me to be the center of attention. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q8 8) I will show off on this social network if I get the chance. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q9 9) I get upset when people don’t notice how I look in photos on this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

158 Q10 10) Participating in this social network allows me to amount to something in the eyes of the world. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q11 11) The time that it takes to display a page on the social network is tolerable. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q12 12) Please estimate how many seconds it takes for pages on average to completely download on this social network. > 15 sec (1) 5 - 15 sec (2) 3 - 5 sec (3) 1 - 3 sec (4) < 1 sec (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

159 Q13 13) I am familiar with the privacy policy of this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q14 14) I feel that this social network’s privacy policy protects my information. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q15 15) The reputation of this social network’s privacy policy is good. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

160 Q16 16) This social network with regards to its privacy policy is trustworthy. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q17 17) Overall, my reaction to this social network is “wonderful”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q18 18) Overall my reaction to this social network is “satisfying”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

161 Q19 19) Overall my reaction to this social network is “stimulating”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q20 20) The organization of the displayed information is “very clear”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q21 21) The sequence of the displayed information is “very clear”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

162 Q22 22) The use of terms throughout this social network is “consistent”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q23 23) It is easy to learn to use this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q24 24) Tasks can be performed easily with this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

163 Q25 25) This social network’s speed is “fast enough”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q26 26) Correcting mistakes on the social network is “easy”. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q27 27) This social network is easy to use. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

164 Q28 28) This social network is easy to operate. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q29 29) Using this social network improves my ability to establish and maintain relationships. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q30 30) Using this social network allows me to catch up with friends more quickly. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

165 Q31 31) Using this social network enhances my effectiveness in relationships. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q32 32) I am willing to put in a great deal of effort in order to help this social network achieve its goals by e.g. filling out optional forms with my personal information. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q33 33) I am willing to help this social network by trying new features that are in beta phase and not finalized. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

166 Q34 34) I am willing to help new users of this social network if they do not know how to do a specific task (e.g. post updates, upload photos, join groups, etc). Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q35 35) I am involved in marketing and recommending the usage of this social network to others. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q36 36) I help other users of this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

167 Q37 37) I can provide input for this social network for new features and products. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q38 38) This social network considers users as part of the product development team. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q39 39) I intend to continue to use this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

168 Q40 40) I predict that I will continue to use this social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q41 41) All things considered I am satisfied with the social network. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q42 42) Overall my interaction with the social network is satisfying. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

169 Q43 43) For me the chance of switching to an alternative social network is low. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q44 44) I share knowledge about this social network with friends, family, or colleagues. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q45 45) In the future, I will recommend the use of this social network to my friends, family, or colleagues. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

170 Q46 46) In the past, I have recommended the use of this social network to my friends, family, or colleagues. Strongly Disagree (2) Neither Agree Agree (4) Strongly Agree Disagree (1) nor Disagree (3) (5) Facebook (1)      Friendster (2)      LinkedIn (3)      MySpace (4)      Ning (5)      Orkut (6)      Twitter (7)      Yahoo!360 (8)     

Q47 47) Please select the month and year, to the best of your recollection, that you started using the social network. Month Year Nov (11) Nov Dec (12) Dec 2011 (1) 2011 (2) 2010 (3) 2009 (4) 2008 (5) 2007 (6) 2006 (7) 2005 (8) 2004 (9) 2003 Oct (10) Oct May(5) Aug (8) Aug Mar(3) Apr (4) Apr Feb (2) Feb Jun (6) Jun Jan(1) Jul (7 Jul Se (9) Se

)

Facebook

(1) Friendster

(2) LinkedIn

(3) MySpace

(4) Ning (5) Orkut (6) Twitter

(7) Yahoo!36

0 (8)

Q48 48) Please select your gender.  Male (1)  Female (2)

Q49 49) Please enter the year you were born.

Q50 50) Please indicate your level of education.  Never graduated high school (1)  High school degree or equivalent (e.g. GED) (2)  Some college (3)  Associates Degree (4)  Bachelors Degree (5)  Masters Degree (including MBA) (6)  Doctorate Degree (7)  Post Doctorate Work (8)

171 Q51 51) How would you classify yourself with regards to technology?  Early Adopter (1)  Neither Early nor Late (2)  Late Adopter (3)

Items Construct

Q1-Q6 Voyeurism

Q7-Q10 Exhibitionism

Q11-Q12 Page Load Time

Q13-Q16 Privacy Policy

Q17-Q26 User Interface

Q27-Q28 Perceived Ease of Use

Q29-Q31 Perceived Usefulness

Q32-Q34 Co-production

Q35-Q38 Co-creation

Q39 - Q42 & Q43 Retention

Q44-Q46 Fan Out

172 Critical Ratio, Heteroscedasticity, & Variance

Critical Ratio Test for metric invariance between sample groups

WSOM Snowball Estimate P Estimate P z-score ZQ4 <--- Voy 0.823 0.000 0.837 0.000 0.287 ZQ6 <--- Exhib 0.494 0.000 0.649 0.000 3.007*** ZQ7 <--- Exhib 0.768 0.000 0.811 0.000 0.912 ZQ8 <--- Exhib 0.711 0.000 0.723 0.000 0.240 ZQ9 <--- Exhib 0.765 0.000 0.728 0.000 -0.778 ZQ10 <--- Exhib 0.662 0.000 0.708 0.000 0.911 ZQ11 <--- Load 0.656 0.000 0.547 0.000 -1.99** ZQ12 <--- Load 0.487 0.000 0.447 0.000 -0.717 ZQ14 <--- Priv 0.702 0.000 0.751 0.000 1.011 ZQ15 <--- Priv 0.820 0.000 0.871 0.000 1.084 ZQ16 <--- Priv 0.851 0.000 0.875 0.000 0.520 ZQ20 <--- UI 0.818 0.000 0.847 0.000 0.604 ZQ21 <--- UI 0.816 0.000 0.795 0.000 -0.436 ZQ23 <--- PEU 0.728 0.000 0.770 0.000 0.919 ZQ24 <--- PEU 0.706 0.000 0.758 0.000 1.114 ZQ27 <--- PEU 0.949 0.000 0.909 0.000 -0.958 ZQ28 <--- PEU 0.896 0.000 0.903 0.000 0.162 ZQ5 <--- Voy 0.909 0.000 0.955 0.000 0.962 ZQ25 <--- Load 0.816 0.000 0.761 0.000 -0.985 ZQ32 <--- Co-prod 0.783 0.000 0.621 0.000 -2.955*** ZQ35 <--- Co-prod 0.737 0.000 0.679 0.000 -1.074 ZQ34 <--- Co-create 0.761 0.000 0.827 0.000 1.370 ZQ36 <--- Co-create 0.859 0.000 0.888 0.000 0.606 ZQ39 <--- IU 0.871 0.000 0.930 0.000 1.465 ZQ40 <--- IU 0.864 0.000 0.910 0.000 1.121 ZQ41 <--- IU 0.844 0.000 0.861 0.000 0.395 ZQ42 <--- IU 0.638 0.000 0.756 0.000 2.508** ZQ43 <--- IU 0.851 0.000 0.931 0.000 1.946* ZQ45 <--- FO 0.950 0.000 0.967 0.000 0.397 ZQ46 <--- FO 0.707 0.000 0.736 0.000 0.611 Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

173 Heteroscedasticity Paths

IV/Mediator Mediator/DV R2 (threshold <.30) Voyeurism Co-creation .002 Voyeurism Co-production .003 Voyeurism Perceived Usefulness .002 Exhibitionism Co-creation .088 Exhibition Co-production .308 Exhibitionism Perceived Usefulness .000 Page Load Time Perceived Usefulness .043 Privacy Perceived Usefulness .005 User Interface Perceived Ease of Use .423 Co-creation Fan Out .259 Co-production Fan Out .232 Co-creation Retention .170 Co-production Retention .064 Perceived Ease of Use Retention .202 Perceived Usefulness Retention .466

Variance Summary

Latent Item Standardized Trait Method Error Total Variable Regression Variance Variance Variance Variance Weight

Voyeurism Q4 0.771 0.594 0.054 0.344 0.993 Q5 0.903 0.815 0.054 0.114 0.984 Exhibitionism Q6 0.381 0.145 0.054 0.529 0.728 Q7 0.706 0.498 0.054 0.464 1.017 Q9 0.668 0.446 0.054 0.416 0.917 Q10 0.635 0.403 0.054 0.538 0.996 Page Load Q11 0.789 0.623 0.054 0.336 1.013 Q12 0.449 0.202 0.054 0.676 0.932 Privacy Policy Q14 0.580 0.336 0.054 0.556 0.947 Q15 0.849 0.721 0.054 0.178 0.953 Q16 0.824 0.679 0.054 0.208 0.941 User Interface Q20 0.810 0.656 0.054 0.299 1.009 Q21 0.708 0.501 0.054 0.443 0.999 Perceived Ease Q23 0.651 0.424 0.054 0.545 1.023 of Use Q24 0.663 0.440 0.054 0.557 1.051 Q27 0.873 0.762 0.054 0.169 0.985 Q28 0.834 0.696 0.054 0.208 0.958 Perceived Q29 0.806 0.650 0.054 0.296 1.000 Usefulness Q30 0.754 0.569 0.054 0.449 1.072 Co-production Q32 0.656 0.430 0.054 0.515 1.000 Q35 0.706 0.498 0.054 0.514 1.067 Co-creation Q34 0.739 0.546 0.054 0.435 1.035 Q36 0.857 0.734 0.054 0.232 1.021 Intent to Use Q39 0.863 0.745 0.054 0.271 1.070 Q40 0.874 0.764 0.054 0.241 1.059 Q41 0.792 0.627 0.054 0.404 1.086 Q42 0.679 0.461 0.054 0.583 1.098 Q43 0.860 0.740 0.054 0.298 1.092 Fan Out Q45 0.939 0.882 0.054 0.086 1.022 Q46 0.679 0.461 0.054 0.576 1.091

174 APPENDIX D: The Influence of User Mix on the Viral Growth of Social Networking Sites

ABSTRACT

Social networking sites (SNS) are designed to provide content services based on an evolving array of user generated content (UGC). Recently such platforms have experienced tremendous growth. At the same time critical economic factors such as pricing that have been traditionally used to explain market growth are inadequate to explain the growth due to free nature of the services. Other common explanations using positive network effects do not go far enough in explaining why some platforms such as Facebook grow fast while others such as Friendster do not despite adopting quite similar strategies. In this study we develop and test a model that builds upon and refines the two- sided models of growth for situations where the exchange value is not monetary. We hypothesize a model that considers organizational factors of co-creation and co- production in the context of different social exchange features supported by the SNS, as well as joint variation in their individual antecedents – voyeurism and exhibitionism – to predict platform growth – when measured by user fan out and retention. In particular, we analyze to what extent platform growth is moderated by differences in the mix of drivers that lead to participate in the co-production process. To this end we explore how the differences in the level of co-creation and co-production moderate the effects of voyeurism and exhibitionism on platform growth. We also investigate how the ratios of user’s propensity towards simultaneous voyeuristic and exhibitionistic motivator affect the fan out and retention. To validate our theory, we test these models with survey data reported by 1449 users of eight separate SNS. The analysis involves use of advanced clustering techniques and SEM analysis of moderated mediation effects. Overall, we make several theoretical, methodological, and practical contributions to the discipline of innovation diffusion associated with SNS services and related models of two-sided markets.

INTRODUCTION

The proliferation of social networking sites (SNSs) has created an unseen social phenomenon, which socially engages hundreds of millions of Internet users around the world (Wilson et al., 2010). SNS can be defined as platforms that enable “online, human- relationship building by collecting useful information and sharing it with specific or unspecific people” (Kwon & Wen, 2010). Such platforms help build and sustain social

175 networks defined as “a social structure of nodes that represent individuals (or

organizations) and the relationships between them within a certain domain” (Liccardi et

al., 2007:225). According to Baldwin and Woodard (2009) digital platforms, such as

SNS, typically include a family of stable components that support a continual assortment of services with the ability to morph new services that allow users to build, make and become visible in their social networks (Boyd & Ellison, 2007). Therefore, SNS platforms are founded on an organizing architecture that helps seamless integration of

services, processes, and new technologies as to enable social interactions and their

growth.

The rapid growth of SNSs has kindled a strong desire to understand platform

evolution and its antecedents (Chesbrough & Spohrer, 2006). This has triggered

multidisciplinary efforts to characterize the nature, positioning, diffusion, and effects of

platforms (Parameswaran & Whinston, 2007; Rai & Sambamurthy, 2006). Additionally, this has generated a growing stream of research both in networks economics (Van den

Bulte, 2010; Van den Bulte & Lilien, 2001; Van den Bulte & Stremersch, 2004) and in

social network studies (Abrahamson & Rosenkopf, 1997; Enders et al., 2008; Hamm,

2008) on factors that promote platform growth. The bulk of these studies address how

platforms enable a growing number of economic exchanges by generating positive

network effects (both direct or indirect) that drive platform growth (for a review see

Baldwin & Woodard, 2009; Boudreau & Hagiu, 2009; Evans, 2009; Gawer, 2010).

The exchange of economic value, however, is not the primary driver for use of

SNSs, because their use is are free and they are intended to facilitate exchanges of

personal or social information and experiences in a “mosaic of social worlds” (Boudreau

176 & Hagiu, 2009). Nass and Moon (2000) also posit that what is seen as an economic exchange on SNS is, in fact, driven by “reciprocal self-disclosure” where users who receive intimate disclosure feel obligated to respond similarly. Therefore, economic

factors such as price that are traditionally used to explain market growth are inadequate.

In line with this research on SNS growth has increasingly focused on addressing jointly social, individual, and technological factors that may influence platform growth (Fisher et al., 2011).

The motivation for this research is grounded in the theories of service-dominant

logic and self-identity. In previous research it has been identified that the motivations for building and maintaining self-identity – voyeurism and exhibitionism – were antecedents for the participation in the value creation process of co-creation and in the evolution of the services through co-production (Fisher et al., 2011). Further it was identified that a

balanced ratio between voyeurism and exhibitionism within people correlated with

greater viral growth such as with Facebook which had a more balanced amount of both

behaviors with a ratio of 2.3 whereas Friendster had a more lopsided 4.5 ratio (Fisher et

al., 2010). However, these studies do not provide the details of whether this exhibitionism

and voyeurism were between people in a social network or within a single user of a social

network nor do they provide insight into how the shifting balance affects the co-creation

of user generated content (UGC). The social exchange process that promotes viral growth

on SNS is a very complex interchange having many nuances that must be understood in

finer detail should we hope to understand the processes at work. To this end we are

driven to study the composition of social exchange processes and their antecedents in

greater detail.

177 Our question in this study is: How do the individual and the network specific

ratios of voyeurism to exhibitionism affect viral growth on the SNS? In conducting the

study, we draw upon concept of service-dominant logics of value exchange (Vargo &

Lusch, 2004). This logic forms a radical departure from a goods-dominant logic, and

argues that value is created with and determined by customer (user) behaviors during the

creation and consumption of content (Lusch & Vargo, 2006b). Accordingly, we are concerned with the antecedents of user involvement in the creation of service content (co- creation) and in the ‘evolving’ of the platform services (co-production). In our previous study we focused on direct and mediating effects of voyeurism and exhibitionism (Fisher et al., 2011). In this paper we investigate the moderating and interaction effects of the voyeurism/exhibitionism ratios on SNS platform growth that offer a better handle how specifically these two drivers together influence viral growth.

The remainder of this paper is organized as follows. In the next section we will investigate the theoretical background and address the relevant theories that we will apply to address our research question. Following this we introduce the key elements of our research model and derive related hypotheses. Then we discuss the research methods that were used to complete the study, report our findings. We conclude by summarizing the results and exploring future research avenues.

THEORETICAL BACKGROUND

The purpose of this review is to identify the extant literature that underpins the theoretical social exchange model will articulate in the next section. Specifically we will define co-creation and co-production processes on a SNS through the review of theories of service-dominant logic. We will also illuminate the constructs of voyeurism and

178 exhibitionism as they apply to participation in SNS through the examination of social

exchange theory (SET). We will next justify the logic behind balancing voyeurs and

exhibitionists on a SNS as suggested by the economic valuation of two-sided markets.

We will also finish by providing a brief synopsis of recent research that identifies

interactions of these constructs as they relate to the viral growth of SNSs.

Customer Participation

The growth of digital services with a shorter development cycles has led to a need to involve customer as a co-producer (Davidow & Malone, 1992; Etgar, 2008; Grönroos,

1993; Hoyer et al., 2010). Co-production is the process whereby consumers actively

contribute to new product features across the whole service development cycle (O’Hern

& Rindfleisch, 2009; Schultze & Bhappu, 2005). By doing so customers become so

immersed in the development of products and services that they co-create the value

(Kambil et al., 1999). Increasingly, firms view co-production as a means to achieve

sustainable competitive advantage (Prahalad & Ramaswamy, 2004). As a result, the

traditional company-centric view of value creation is becoming obsolete and replaced by

one in which consumers change from being passive recipients of services to active contributors (Prahalad & Ramaswamy, 2004).

Lusch and Vargo (2006a), distinguish further between the co-creation of value during the consumption phase, and its co-production during the design phase. The unique value of co-creation is the experience itself (Prahalad & Ramaswamy, 2003), whereas the unique value of the co-production is expansion or modification of the future experience.

The value in co-creation within the SNS is the constant creation of UGC. It is influenced among others on the level of user’s voyeuristic and exhibitionistic behaviors (Fisher et

179 al., 2010). The value of co-production is that ultimately the service will contains desired

attributes or features and in the context of SNS it will promote growth and new user

experiences. Co-production thus can be viewed as involving customers in roles typically played by product managers or service designers.

User Motivations

Per Zwass (2010), we posit there is a wide array of user motivators for creating

and consuming UGC. In addition, these are different from a set of factors that explain the

participation in the exchange of economic value. Participation in the exchange of

economic value is about maximizing utility and/or minimizing cost, whereas the

participation in the creation and consumption of UGC is about building bonds of

reciprocity and projection of self-identity. The positive experiences of engaging in

consuming service derive, indeed, from the act of co-creation which will increase the

user’s likelihood to recommend the service to friends, family, and colleagues (fan out)

and to regularly return to use the services (retention) (Fisher et al., 2010).

Overall a SNS platform can be considered a two-sided market that comprises of

three sort of exchanges that take place between three types of dyads: 1) the trade of

“eyeballs” between users and advertisers (Rysman, 2009); 2) the exchange of information

about service usage between users and the platform owner or other developers of the

platform (Zwass, 2010); 3) and the exchange of UGC between users, motivated among

others by voyeuristic and exhibitionistic behaviors, (Calvert, 2004). We have previously

proposed that on SNS the user-user dyad exchange of UGC forms the ‘foreground’

process on the site and it takes place through user’s engaging in co-creation driven by

largely voyeuristic and exhibitionistic motivations (Fisher et al., 2011). Although there is

180 a predilection for voyeuristic behavior as observed in Fisher et al. (2012), we can argue also for the ‘reciprocal self-disclosure in this process’, where people who receive intimate disclosure of personal information feel obligated to respond in a similar manner (Nass &

Moon, 2000). This creates a balance of co-creation on both sides of the dyad that spurs viral growth in that people will return to the site and will recommend it to others (in the hope of similar exchanges).

The two-sided market for the co-creation of UGC need to be structured as to allow voyeurs to provide ‘eyeballs’ and occasionally feedback through ‘meta-content’ as to reinforce the projected self-identity of the exhibitionist. Exhibitionism, indeed, implies

the need for someone to watch and assumes and reinforces voyeuristic behaviors

(Holbrook, 2001). The social exchange of UGC on a SNS starts thus with one participant

making an exhibitionistic display move; if the voyeurs respond through reciprocation or

encouragement, new rounds of exchange will follow (Cropanzano & Mitchell, 2005).

To further clarify the motivations of exhibitionists and voyeurs, we will draw

upon Social Exchange Theory (SET). The basic premise of the SET is that “each

individual in a dyad engages in a diverse set of interpersonal interactions or exchanges in

order to influence his or her partner and attain the most favorable outcomes – that is, to

maximize rewards and minimize costs” (Feeney & Noller, 2004). A number of variations of SET since it was first proposed by Thibaut and Kelly (1959) have been made including reinforcement theory (Homans, 1958), interdependence theory (Jowett & Cramer, 2009),

and interpersonal exchange model of sexual satisfaction (Lawrance & Byers, 1995).

These SET models share in common that individual’s motivations in participating in

social exchange are: 1) the desire to gain resources and 2) to define their social identity

181 (Tyler, 2001). In applying this theory we will focus more on the second goal as it often

helps in achieving the first goal. For example, sharing information as an expert

contributes towards the formation of a person’s self-identity as an expert which arises from the need for self-expression (Constant et al., 1994). At the same time such sharing builds networks to other experts or stakeholders, which helps future access to resources.

The fundamental element of the social identity draws upon the concept of self- identity which owes its origin to William James’ (1890) book The Principles of Psychology.

James conceived this concept of the ‘empirical self’ as consisting of the material, social, and spiritual self. The idea that one’s self-identify is comprised of multiple selves is reflected in James’ (1890:294) oft cited statement that an individual "has as many social selves as there are individuals who recognize him." Fisher et al. (2012) indeed found that user’s work on their self- identity formed a significant predictor of viral growth in that

SNS users seek to express their self-identity through the roles of being entertainer and being entertained – exhibitionist and voyeur – by UGC.

The roles of the entertained and entertainer are interlinked by mutuality of interaction as assumed in SET, but in some cases people choose exclusively either the performer or the audience roles. In this case there is not a mutual expectation of reciprocity (Prus, 1997). The entertaining individual seeks to develop her ‘voice’ based upon her audience’s interest and at times she must deal with difficult or upset audience.

The entertained self will seek out the many entertainers, and therefore revisit frequently the site to consume the entertainment. She may quickly lose interest unless the entertainer knows how to sustain her attention by making the content inspiring and meaningful.

182 As noted the two basic components in SET are the balance of rewards and costs –

e.g. economic value – and equality – e.g. rules of reciprocity. These rules generate trust

and loyalty within relationships (Emerson, 1976) with the most fundamental rule being

that of reciprocity or equality. Equity models propose that a dyad’s interactions are not

driven exclusively by each attempting to maximize their own rewards or minimize their costs; rather they are also driven by the desire to maintain equality – respect – in the relationship. Equality is a disruptive justice norm that plays an important role in intimate relationships (Deutsch, 1975) and a relationship is deemed equitable when both partners receive the same level of outcomes regardless of inputs (Sprecher, 1998). In a SNS the exhibitionist often provides much greater input, but both the voyeur and the exhibitionist result in equal causatum as their desire to participate in the exchange is different but rewards somewhat equal.

While the rule of reciprocity in SET can be balanced without equal exhibitionistic actions and some people choose predominantly either the performer or the audience roles, we are unclear whether the levels of voyeurism and exhibitionism are dictated by the intrinsic nature of the individual or the affordances from the environment. A growing body of theory and research has lent support to the idea that many aspects of the self are a cultural creation (Heine, 2001). As Papacharissi (2011) states “the process of self- presentation becomes an ever-evolving cycle through which individual identity is presented, compared, adjusted, or defended against a constellation of social, cultural, economic, or political realities.” Individuals create a “face” for different interactions and a variety of situations and when they play the same part to the same audience a social relationship is likely to be established (Goffman, 1959). In a SNS the users are afforded

183 opportunities to display both voyeuristic and exhibitionistic behaviors, but the literature

does not agree with regard to whether the level of each behavior is an intrinsic trait of the

users or extrinsically motivated by the SNS.

In summary, the two-sided market concept and SET offer a promise to explain

SNS growth by including suggestions why individuals participate in SNS use – it

involves the development and maintenance of self-identity as codified through the process of co-creation driven by voyeurism and exhibitionism. A theoretical model that combined the constructs of service-dominant logic – co-creation and co-production – with the antecedents of voyeurism and exhibitionism – has been shown to have greater explanatory power of the viral growth factors of fan out (16% improvement) and retention (33% improvement) than the technology adoption model (TAM) alone (Fisher et al., 2011). While this offers significant insight into the factors that drive the viral growth of SNS, the subtleties of how reciprocity affects the self-identity processes are still unknown. In order to further clarify viral growth of SNS platforms with these

theories require detailed conceptual development and related augmentation. We will next

do so by investigating next how the concepts of ratios of voyeurism to exhibitionism –

both within individual users and between groups of users – will affect the viral growth of

SNS. Next we will outline the motivation for the inclusion of the concept of ratios and

articulate a new model founded on this concept.

MODEL DEVELOPMENT AND HYPOTHESES

In this section we will develop a set of models with associated hypotheses that

explain how different ratios of voyeurism and exhibitionism – both between and within

users – affect the co-creation and co-production that have been show to drive the viral

184 growth factors of fan out and retention. As noted, a healthy ratio of voyeurism and

exhibitionism between and within users was identified as a promoting factor in viral

growth for the SNS during qualitative research (Fisher et al., 2010). Further, it was

identified that these individual motivations were antecedents of the social exchange

processes of co-creation and co-production that drove fan out and retention (Fisher et al.,

2011). In order to hypothesis how these ratios influence co-creation and co-production,

we need to create a typology that classifies users of SNS in relation to their within and between ratios of voyeurism and exhibitionism.

To group the ratios of voyeurism and exhibitionism between users RB(v/e) (ratio of

voyeurism and exhibitionism between users) and within users RW(v/e) (ratio of voyeurism

and exhibitionism within users) we used a combination of hierarchical and

nonhierarchical clustering techniques as described in the data analysis section below. We

expect that the clustering will result in combinations of user behaviors that are binary –

either the users participate in voyeurism or they do not and they either participate in

exhibitionism or not. Therefore, we predict the combinations will consist of: (1) high

voyeurism / high exhibitionism (2) low voyeurism / high exhibitionism, (3) high

voyeurism / low exhibitionism, or (4) low voyeurism / low exhibitionism. For within

users RW (v/e) we conceptualize a framework consisting of four distinct categories as

shown in Table D1. We characterize each group of users starting with the ones that

appear to have the highest levels of both voyeurism and exhibitionism. We coin them

engaged, because they are fully engaged with both behaviors. For those with low

voyeurism and high exhibitionism, we denote them as exhibitionists. In contrast, for those

with high voyeurism and low exhibitionism, we label them as voyeurs. Lastly, for users

185 with low voyeurism and low exhibitionism, we label them as unengaged, because they

apparently do not actively participate in the SNS use through behaviors that relate to their identity formation, projection, and exploration.

TABLE D1: Within User Voyeur to Exhibition Ratio

Voyeurism Exhibitionism User Typology

High High Engaged Low High Exhibitionist

User High Low Voyeur Within a Low Low Unengaged

Similarly, for between users RB(v/e) , we conceptualized a framework of four

distinct categories as shown in Table D2. We developed this typology based on the level of the two key discriminatory factors – voyeurism and exhibitionism. The SNS that exhibited high voyeurism and high exhibitionism between users were called balanced, because the users were equally balanced on each of the behaviors. For those SNS that exhibited low voyeurism and high exhibitionism, we labeled runway, as more users were

“showing off” their UGC than watching. In contrast, for those with high voyeurism and low exhibitionism, we labeled paparazzi, because more users were watching than generating UGC. Lastly, for those with low voyeurism and low exhibitionism, we labeled reserved, because there was neither creation nor consumption of UGC.

TABLE D2: Between Users Voyeur to Exhibition Ratio

Voyeurism Exhibitionism SNS Typology High High Even-handed Low High Runway

users High Low Paparazzi Between Between Low Low Reserved

186 Our hypothesized model, shown in Figure D1, is based on the results of our previous research (Fisher et al., 2011) and the reciprocity rule of behavior from SET. We only used the social exchange constructs (voyeurism, exhibitionism, co-creation, and co- production) in this model dropping the TAM constructs (perceived ease of use and perceived usefulness) as well as the antecedents (privacy policy, user interface, and page load time) from our earlier study (Fisher et al., 2011). Our rationale for this is that from previous testing we know that the social exchange model resolves similarly with regards to the constructs impact on fan out and retention with or without the TAM constructs.

Additionally, we suspect that our grouping of users and SNS would result in some groups with fewer data points and therefore we wanted to have enough power to test the model with as few constructs as necessary.

FIGURE D1: Hypothesized Model

187 Hypotheses of Voyeurism and Exhibitionism between Users

From our previous quantitative analyses we understand that across all SNS in our

study, the individual behavior of exhibitionism positively affected the platform processes

of co-creation and co-production (Fisher et al., 2011). Co-production, in turn, positively

influenced the viral growth factors of fan out and retention. Conversely, the individual

behavior of voyeurism negatively affected the co-creation process. This is likely the

result of users who primarily exhibited voyeuristic behaviors but were not interested in

generating UGC through the co-creation process.

The results of this previous work inform our hypotheses that SNS with higher levels of exhibitionism (e.g. the runway group with low Voyeurism and high

Exhibitionism) will result in the having the largest positive effect on both co-creation and co-production from the construct of exhibitionism.

Users who desire to practice exhibitionistic behaviors that the service prohibits will not be motivated to return. However, if the service allows for the misuse of services through co-production, e.g. users misusing “friends” and “groups”, these users would want to continue using the platform services. Thus we hypothesize:

H1a – SNS with low Voyeurism (V) and high Exhibition (E) between users (runway) will have a greater positive effect from Exhibitionism (E) to Co- Production (CP) than other SNS.

Prior research has suggested that co-creation of content facilitates the self validation, management of one’s self-identity, and control through persuasive communications (Calvert, 2004). Explicit exhibitionism in the physical world is generally egregious to societal norms, but exhibitionism in the virtual world has been described as

“an avenue toward body acceptance and appreciation that involves the user in a process

188 of collaborative identity formation” that liberates the user from repressive systems of

social control (Jones, 2010:253). Thus exhibitionism facilitated through the “hidden”

services of a digital platform such as an SNS, the process of co-creation can be disguised

to make it social acceptable. Therefore, we posit:

H1b – SNS with low Voyeurism (V) and high Exhibitionism (E) between users (runway) will have a greater positive effect from Exhibitionism (E) to Co-creation (CC) than other SNS.

Because individuals involved primarily in voyeuristic behaviors view other users’

personal information and pictures, there is little opportunity to misuse a product feature

and therefore there is no affect on co-production from voyeurism. However, previous

research has shown voyeurism to have a negative effect on co-creation. These findings

suggest that users who primarily engage in voyeuristic behaviors do not drive the creation

of UGC through the co-creation of value process. In recent research, the term mediated

voyeurism has been used to describe the consumption of images and information of other

peoples’ supposed real lives for entertainment, but at the expense of privacy (Calvert,

2004), which challenges the traditional divisions between the public and the private personal content (Munar, 2010) and changes the conventional code of what can or cannot be shown and to whom (Koskela, 2002). On this we form our opinion that the SNS with the highest levels of voyeurism (e.g. the paparazzi group with high Voyeurism and low

Exhibitionism) will have the greatest negative effect on co-creation. We hypothesize:

H1c – SNS with high Voyeurism (V) and low Exhibition (E) between users (paparazzi) will have greater negative effect from Voyeurism (V) to Co-creation (CC) than other SNS.

189 Hypotheses of Voyeurism and Exhibitionism within Users

The rules of reciprocity as put forward by SET generate trust and loyalty within relationships (Emerson, 1976) and are driven by the desire to maintain equality or balance in the relationship. This desire to create a feeling of equanimity is an individual motivation and can feel balanced to one user, but not to another, tangentially perceived inequality in relationships has been shown to cause burnout in organizational settings

(Schaufeli et al., 1996). Thus we believe this reciprocity rule will affect the ratio of voyeurism and exhibitionism within user instead of between users.

We believe SNS that have users who within themselves have a balance in the behaviors of voyeurism and exhibitionism will have greater fan out and retention, leading to greater viral growth. Although there is a predilection for voyeuristic behavior as we identified in our qualitative research, we hypothesize that when people receive intimate disclosure they feel obligated to respond (Nass & Moon, 2000). Thus, we propose that a balance within users of voyeuristic and exhibitionistic behaviors will create a balance on both sides of the user-user dyad that spurs viral growth.

Informed by the rule of reciprocity from SET we then reapply insights from our previous quantitative research where we demonstrated that the individual behavior of exhibitionism positively affected the platform processes of co-creation and co- production. We therefore expect users with balanced ratios of voyeurism and exhibitionism at higher levels will have a greater positive effect on co-creation and co- production. Thus we hypothesize:

H2a – Users with a balance of high Voyeurism (V) and high Exhibition (E) (engaged) will have a greater positive effect from Exhibitionism (E) to Co- Production (CP) than other Users. 190 H2b – Users with high Voyeurism (V) and high Exhibition (E) (engaged) will have a greater positive effect from Exhibitionism (E) to Co-creation (CC) than other Users.

In our previous study we found, conversely to exhibitionism, that the individual

behavior of voyeurism negatively affected the co-creation process. This, we suspect, is

due to users having primarily exhibited voyeuristic behaviors were not interested in

generating UGC through the co-creation. Therefore, we expect users with imbalanced ratios of voyeurism and exhibitionism and specifically high voyeurism and low exhibitionism (voyeurs) will have a greater negative effect on co-creation, since they will be the least motivated group of users to participate in the generation of UGC. Thus we hypothesize:

H2c – Users with high Voyeurism (V) and low Exhibitionism (E) (voyeurs) will have greater negative effect from Voyeurism (V) to Co-creation (CC) than other Users.

Intrinsic or Extrinsic

Cultural psychology views the individual as containing a set of biological

potentials that interact with situational contexts constraining or affording the demonstration of various patterns of behavior, the focus being on the affordances from the cultural environment (Heine, 2001). When individuals interact they seek to acquire additional information about the other person in order to determine the individual’s concept of self, attitude, competence, etc. but the giving of information is situational dependent and can include misinformation through deceit and feigning (Goffman, 1959).

Thus implying that the amount of exhibitionistic behavior is dependent on the environment of the situation and the other individuals involved. However, in some cases

191 people choose exclusively either the performer or the audience roles in which case there

is not a mutual expectation of reciprocity (Prus, 1997).

We believe that the SNS, as an extrinsic environment, is primarily responsible for

the ratio of voyeuristic and exhibitionistic behaviors in individuals. While users may

display or select predominant ratios of these behaviors the range is likely to change based

on the SNS in which they are participating. Thus we hypothesis:

H3 – The amount of Voyeurism (V) and Exhibitionism (E) behaviors displayed is primarily driven by the SNS rather than the individual.

RESEARCH DESIGN AND METHODS

Data Analysis

To empirically test the proposed models, we surveyed users of five of the 2010

top social networking sites--- Facebook, Twitter, LinkedIn, MySpace, and Ning16 and three social networks that had failed to achieve sustained viral growth in the United

States (e.g. Friendster, Yahoo! 360, Orkut) (see Appendix A). To this end we followed a psychometric survey methodology that maps individual responses to the underlying constructs within our model. Our model involved 6 constructs all of which were measured with reflective scales.

Construct Definition and Operationalization

Dependent Variables

The social contagion variable, retention, defined as the number of users who return to use the service during a specific period of time (Jurvetson, 2000), was operationalization using a scale from Vatanasombut, et al. (2008). To measure the social

16 According to http://www.ebizmba.com/articles/social-networking-websites 192 contagion variable fan out, defined as the number of new users invited per existing user

(Penenberg, 2009). We borrowed items that Cheng and Chen (2007) developed for the

knowledge sharing of technologies related to health the number of new users invited

Independent Variables and Mediating Variables

The construct of voyeurism, which has its origin as a paraphilia in psychology

(American Psychiatric Association, 2000) has not been operationalized in the context of technology adoption nor in the context of service platforms. We adapted five items from

Kim and Rubin (1997) and three items from Chung (2008). To measure exhibitionism, we

chose four items with the highest loading values on the Narcissistic Personality Index

(Kubarych et al., 2004)

We adapted the items to focus on establishing and maintain relationships. To

measure co-production, we utilized a three-item scale operationalized by Cheung and To

(2010). Co-creation was measured using the four-item scale of Singh and Koshy’s (2010)

Given the nested nature of co-production and co-creation (Hilton & Hughes, 2008), items

were modified to better fit the digital service platform context.

Controls

In our model we controlled for users education, age, gender, and date they joined

a social network. Age was quantified as the year the user was born. Education level was

measured using an eight point scale ranging from “never graduated high school” to “post

doctorate work”. We asked the user the year and month they joined each social network

and their gender.

193 Measurement and Instrument Development

Items selected for the constructs were adapted from prior studies in order to ensure content validity. As these constructs had never been operationalized and used in the context of social networking platforms, care was taken to develop reliable and valid measures following the procedures suggested by DeVellis (2011).

The initial item pool consisted of 29 items that measured the 6 different constructs and 4 controls. We conducted several rounds of pretesting using concurrent verbal protocol content analysis (Bolton, 1993). As a result, four items were modified slightly to address problems with comprehension and judgment.

Sample

194 scientific sampling procedures as ordinary sample (Coleman, 1958). Using this method we received 432 responses.

Second, we distributed the survey via email to 229 undergraduate and 618 graduate students at the Weatherhead School of Management. We received 343 completed responses. To maximize response rates, we guaranteed anonymity, collected no personally identifiable information, and assured respondents that only the researchers would have access to the raw data.

The utilization of electronic distribution of surveys via online and email is widely used as it offers researchers low cost, good response rates, and quick response times

(Sheehan & McMillan, 1999). Our response rate of 40.4% is well within the expected response rates for email surveys, which vary from a low of 6 % (Tse, 1995) to a high of

75 % (Kiesler & Sproull, 1986).

10/29 items. Of these 10 items, none comprised all items on any single construct, and were distributed as follows: 3 voyeurism, 3 exhibitionism, and 4 on retention. The samples did not differ significantly for gender, social networks participation, or the date they joined the networks. A significant difference between samples means was observed for year born (“snowball” sample = 1980; WSOM sample = 1983, t-value = 3.7, df=625, p < 0.001). Further, a confirmatory factor analysis (CFA) model tested with both samples was configurally invariant based on model fit (χ2 = 2283, df=720, p < 0.000,

CMIN/DF=3.172, CFI=.941) and metric invariant based on critical ratios (see Appendix

195 C) across both samples. We elected to merge the respondents from both samples and treat them as a single group.

196 TABLE D3: Demographics

Clustering

Kaufman and Rousseeuw (1990) define cluster analysis as the classification of similar objects into groups, where the number of groups, as well as their forms are both unknown. The “form of a group" refers to the parameters of a cluster such as cluster- specific means, variances, and co-variances. In order to detect clusters of users and platforms based on voyeurism and exhibitionism, we used a statistical technique known as latent class cluster analysis (Vermunt & Magidson, 2005). This technique is an advanced Q-factoring technique where data points are clustered according to the range of

197 values on a select set of variables (Hair et al., 2010). Latent class clustering – also known as finite mixture modeling – belongs to a class of relatively new statistical methods for

forming a cluster or a latent class in which there are unobservable (latent) subgroups or

segments within a sample. Cases within the same latent class are homogeneous on certain

indicators, and cases in different latent classes differ statistically on those indicators.

One main difference between latent class clustering and traditional clustering

methods, such as K-clustering, which are totally inductive, is that the latent class

clustering technique involves postulating an a-priori model (specification), and thereby it

assumes a model of probable distributions between clusters based on the set of indicators

(Vermunt & Magidson, 2005). As such, latent class clustering is more akin to

confirmatory factor analysis whereas R-factoring techniques resemble more exploratory

factor analysis (Hair et al., 2010). Therefore, this technique offers statistical tests that can

be used to determine the model fit, indicating the degree to which the resultant set of

clusters are an accurate representation of the data set. Latent class clustering also

overcomes limitations of scaling and distribution inherent in traditional clustering

techniques (Vermunt & Magidson, 2005).

We used the software package Latent Gold, developed by Vermunt and Magidson

that has been mainly used in fields such as psychology and medicine (for example to

examine eating disorders (Mitchell et al., 2007) and post traumatic stress disorder

(Breslau et al., 2005)), in order to analyze rich unstructured data sets. This technique is

particularly useful in determining whether an underlying (latent) profile exists upon

which clusters can be formed in order to separate the data points into classes. We based

198 our class description for within users on voyeurism and exhibitionism and used platform

as an indicator for analyzing voyeurism and exhibitionism between users.

Within Users

Using Latent Gold, we ran multiple models simultaneously specifying clusters

numbering 1 through 10 in order to determine the optimum number of classes, see

summary in Table D4. One criterion for determining the number of clusters is to look at the p-value for each model under the assumption that the L² statistic follows a chi-square distribution. Vermunt and Magidson (2005) provide guidance that in general, among models for which the p-value is greater than 0.05, which indicates an adequate fit, the one

model that is most parsimonious based upon the fewest number of parameters would be

selected.

TABLE D4: Summary of Clustering Models for Within Users

LL BIC(LL) Npar L² Df p-value Class.Err. Model1 1-Cluster -6178.5038 12597.2023 33 813.3540 252 2.6e-60 0.0000 Model2 2-Cluster -5977.9002 12217.8310 36 412.1468 249 3.5e-10 0.1131 Model3 3-Cluster -5907.6673 12099.2011 39 271.6811 246 0.13 0.1307 Model4 4-Cluster -5904.9546 12115.6116 42 266.2557 243 0.15 0.2185 Model5 5-Cluster -5891.0601 12109.6585 45 238.4667 240 0.51 0.1597 Model6 6-Cluster -5888.2495 12125.8731 48 232.8454 237 0.56 0.2536 Model7 7-Cluster -5880.9574 12133.1250 51 218.2614 234 0.76 0.1850 Model8 8-Cluster -5879.6248 12152.2955 54 215.5960 231 0.76 0.2542 Model9 9-Cluster -5880.1810 12175.2438 57 216.7084 228 0.70 0.4234 Model10 10-Cluster -5883.2220 12203.1617 60 222.7905 225 0.53 0.4113 *Log-Likelihood (LL), Bayesian Information criterion (BIC), Number of Parameters (Npar), Likelihood-ratio statistic (L2), Degrees of Freedom (df), Proportion of Classification Errors (Class. Err.)

We tested the indicators of the 3 and 4 cluster models to ensure that they provided

adequate fit and power. While both models were adequate, the 4 cluster analysis provided

2 2 greater total explanatory power of the variability as indicated by higher R values (R Voy

=0.70 Exh=0.46). Additionally, this model fits our theoretical explanation and therefore

was selected. The profile plot of clusters for this model is shown in Figure D2. 199 Voyeurism and Exhibitionism are plotted at two points on the X-axis and each one of the

four clusters is plotted on the grid based on their level of voyeurism and exhibitionism.

The line closest to the X-axis is the cluster that is low in voyeurism and exhibitionism, while the line furthest above is high in both voyeurism and exhibitionism. The two lines in between the extremes represent clusters of medium voyeurism and medium

exhibitionism and high voyeurism and medium exhibitionism.

FIGURE D2: Four Cluster Model Profile Plot

Utilizing the four cluster model, we assigned each user to a class and assigned

each class a label from our Within User Typology. The results of this clustering are shown in Table D5.

200 TABLE D5: Clustering for Voyeurism & Exhibitionism within Users

Voy Exh Label Size Cluster 1 M M Moderate 634 Cluster 2 H M Voyeur 411 Cluster 3 L L Unengaged 310 Cluster 4 H H Engaged 94

Between Users

To cluster between users for voyeurism and exhibitionism, we ran multiple models specifying clusters numbering 1 through 5 using the indicators of platform and exhibitionism, as shown in Table D6, and then repeated the analysis using platform and voyeurism as seen in Table D7.

TABLE D6: Summary of Clustering Models for Exhibitionism between Users

LL BIC(LL) Npar L² Df p-value Class.Err. Model1 1-Cluster -3460.9548 7074.7607 21 125.9654 147 0.89 0.0000 Model2 2-Cluster -3449.5289 7117.4166 30 103.1137 138 0.99 0.1398 Model3 3-Cluster -3448.0549 7179.9764 39 100.1658 129 0.97 0.3736 Model4 4-Cluster -3447.9662 7245.3065 48 99.9882 120 0.91 0.3673 Model5 5-Cluster -3447.4087 7309.6992 57 98.8732 111 0.79 0.4854

TABLE D7: Summary of Clustering Models for Voyeurism between Users

LL BIC(LL) Npar L² Df p-value Class.Err. Model1 1-Cluster -2717.5490 5522.4416 12 104.0366 84 0.068 0.0000

Model2 2-Cluster -2692.6215 5538.0942 21 54.1816 75 0.97 0.1748 Model3 3-Cluster -2688.0456 5594.4501 30 45.0298 66 0.98 0.1988 Model4 4-Cluster -2686.6650 5657.1966 39 42.2687 57 0.93 0.3056

Model5 5-Cluster -2684.9489 5719.2721 48 38.8365 48 0.82 0.3443

201 We chose the 3 cluster models for both voyeurism and exhibitionism between users as they had adequate fit, were parsimonious, and supported the classification of high, medium, and low that resulted from the within users clustering. Utilizing this cluster model, we assigned each user to a class, assigned each class a label from our between user typology, and combined the platform characteristics from Figure D2 of social content, user activity, and user engagement with the clustering results. This summary is shown in Table D8.

TABLE D8: Clustering Between User

Social User Behaviors SNS Typology Size Network Exhibitionism Voyeurism Facebook (1) H M Even-handed 623 Twitter (7) M H Paparazzi 242 Orkut (6) M H Paparazzi 17 LinkedIn (3) M L Runway 333 Ning (5) M L Runway 14 Yahoo!360 (8) H L Runway 20 MySpace (4) L M Reserved 170 Friendster (2) L L Reserved 30

Measurement Models

Before proceeding to the measurement model analysis, time was spent in

"cleaning" the data. Outliers can be removed by analyzing their z-score relative to a standard population, or by using formal outlier analysis such as the Dixon test (1950).

Cohen (2003:128) suggest that “if outliers are few (less than 1% or 2% of n) and not very extreme, they are probably best left alone.” Because our questions were Likert scale responses and outliers were less than 1%, we did not delete any outliers. Since we intend to analyze the data with techniques that involve a normality assumption, it was necessary to correct the problem of skewness for two items (Q6, Q9) by applying a square 202 transformation. We analyzed Mahalanobis distance of data and removed the top one hundred data points farthest from the centroid. Since their removal did not improve model fit, they were retained for further analysis. We also tested for heteroscedasticity of indicators and there was some threat to the model from several paths (see Appendix A).

We tested finally for multicollinearity among indicators and did not find significant threat and did not remove any variables as all combinations had low variance inflation factor

(VIF) < 1.53.

Exploratory Factor Analysis

We began our measurement model development first by conducting an EFA and then a CFA. Because we were interested in identifying latent reflective constructs expected to produce scores on underlying measured variables (Fabrigar et al., 1999;

Tabachnick & Fidell, 2007) and interested in the shared variance (Costello & Osborne,

2010), we used common factor analysis (Hair et al., 2010). Principle Axis Factoring

(PAF) technique was used as factors were assumed to be non-orthogonal (correlated). We chose oblique rotation, because of its assumption of correlated variables. Promax, an oblique rotation technique, and we applied the latent root criterion (factors with

203 eigenvalue less than 1.0 are not included) as well as the scree analysis to determine how

many factors to retain.

The factorability of 17 items was examined for the 6 constructs, as shown in

Table D9. Several well-recognized criteria for the factorability of a correlation were used.

First, 100% of items correlated at least .30 or higher with at least one other item, suggesting reasonable factorability. Secondly, the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy was 0.909, above the recommended value of .60, and Bartlett’s test

2 of sphericity was significant (χ (435) = 26162.538, p < .000). The diagonals of the anti-

image correlation matrix were all over 0.65, supporting the inclusion of each item in the

factor analysis. Finally, the communalities were all above 0.30 further confirming that

each item shared some common variance with other items.

While low inter-factor correlations allow the researcher to assume orthogonality and use orthogonal models like Principal components analysis (PCA) for analysis

(Fabrigar et al., 1999), we observed a significant number of correlations greater than .30

(100%) suggesting non-orthogonality. Thus, we continued our analysis with an oblique rotation using Principle Factor Analysis (PFA). Moreover, our primary purpose was to test a theoretical model (Tabachnick and Fidell 2007). A promax rotation provided the best defined factor structure. The results showed an 11 factor solution with eigenvalues greater than 1.0 and the total variance explained was 64.622%. Two items (Q35 and Q34) were removed due to cross loading during EFA.

In the final model all factors loadings were above 0.5, demonstrating convergent validity (Bagozzi & Yi, 1988), while most of the factor loadings exceeded 0.7,

which Hair et al. (2010) consider indicative of a well-defined factor structure. Two

204 motivations led the author to retain some items with factor loadings below 0.7. First from

a statistical perspective, it is recommended that researchers should design studies that

achieve a minimum statistical power level of 80 percent (Hair et al., 2010). Statistical power is influenced by sample size. Specifically, a factor loading of 0.3 for a sample size of 350 or greater achieves 80 percent statistical power (Hair et al., 2010:117). Therefore, factor loadings below 0.3 were still capable of producing sufficient statistical power (>80 percent) given the large sample size (n=750). This leads to the second motivation for retaining these items in the study. Hair et al. (2010:117) suggests that researchers “should realize that extremely high loadings (0.80 and above) are not typical and that the practical significance of the loadings is an important criterion.” In the case of this study, the items in question maintained practical significance to their respective constructs as well as tapped into phenomenon under study. Table D9 below summarizes the results of the

EFA.

205 TABLE D9: EFA & CFA Summary

Construct Item Mean Standard Loading Reliability Composite Average Maximum Average Deviation Coefficient Reliability Variance Shared Shared (Cronbach Extracted Variance Variance Alpha) Criteria > 0.50 > 0.70 > 0.70 > 0.50 < AVE < AVE Voyeurism Q4 2.30 1.144 .858 .863 .87 .77 .39 .07 Q5 2.09 1.089 .829 Exhibitionism Q6 1.54 .898 .623 .804 .81 .52 .43 .13 Q7 2.26 1.145 .606 Q9 1.78 1.000 .911 Q10 2.12 1.226 .646 Co- Q32 2.33 1.057 .504 .682 .68 .52 .44 .21 production Q35 2.64 1.255 .692 Co-creation Q34 3.36 1.063 .930 .830 .83 .71 .44 .22 Q36 3.23 1.092 .768 Retention Q39 3.70 1.214 .972 .930 .93 .72 .72 .25 Q40 3.64 1.041 .818 Q41 3.59 1.021 .785 Q42 3.49 1.149 .645 Q43 3.67 1.221 .985 Fan Out Q45 3.41 1.120 .587 .819 .83 .72 .72 .26 Q46 3.56 1.107 .567 Model Fit Statistics (CFA) Threshold Results Reference Chi Square (Deg of Freedom) 830.716 (347) Probability <0.05 0.000 Sample Size (n) > 5*items 699 Hair et al (2010) CMIN/DF < 2 2.321 Tabachnik & Fidel (2007) CFI > 0.95 0.967 Hu & Bentler (1999) PCFI > 0.5 0.771 Hu & Bentler (1999) RMSEA (LO 90 – HI 90) < 0.06 0.043 (.039-.047) Hu & Bentler (1999) PCLOSE > 0.5 0.999 SRMR < 0.09 0.0366 Hu & Bentler (1999)

Confirmatory Factor Analysis

We performed a CFA using the results from our EFA analysis. The sample size of

699 was deemed sufficient given the low communalities (Hair et al. 2010) and acceptable

Hoelter's benchmark for Critical N, which should exceed 200 (Nokelainen, 2009)

(CN=482, p<.01). The fit of the CFA model was acceptable with sufficient convergent and discriminate validity, (Appendix C).

During CFA we consulted modification indices to co-vary error terms within the same construct – retention. This improved the model fit significantly. The theoretical basis for co-varying these items is that they deal with how quickly mistakes can be

206 corrected and how quickly friends can be connected. These were closely related and

therefore respondents are likely to answer them similarly.

Confirmatory Factor Analysis: Reliability and Validity.

Fornell and Larcker (1981) listed three statistics to assess convergent validity:

item reliability of each measure, composite reliability of each construct and the average

variance extracted. These are item reliability of each measure, composite reliability of

each construct, and the average variance extracted. Hair et al. (2010) suggested that an

item is significant if its factor loading is greater than 0.50 and the sample size is

reasonably large. As shown in Table D9, the factor loadings of all the items in the

measure range from 0.504 to 0.985, thus meeting the threshold set by Hair et al., and

demonstrating convergent validity at the item level. At the construct level, Hair et al.

recommended that the composite reliability should be used in conjunction with SEM to

address the tendency of the Cronbach’s alpha to understate reliability. For composite

reliability to be adequate, a value of .70 and higher is recommended (Nunnally &

Bernstein, 1994). The final indicator of convergent validity is the average variance

extracted, which measures the amount of variance captured by the construct in relation to

the amount of variance attributable to measurement error (Fornell and Larcker 1981).

Convergent validity is judged to be adequate when average variance extracted equals or

exceeds 0.50 (i.e. when the variance captured by the construct exceeds the variance due

to measurement error). As shown in Table D9, the convergent validity for the proposed constructs of this study is adequate.

Fornell, Tellis, and Zinkhan (1982) state that discriminant validity is considered adequate when the variance shared between a construct and any other construct in the

207 model is less than the variance that construct shares with its measures. The variance shared by any two constructs is obtained by squaring the correlation between the two constructs. The variance shared between a construct and its measures corresponds to average variance extracted. As such, discriminant validity was assessed by comparing the square root of the average variance extracted for a given construct with the correlations between that construct and all other constructs. Table D10 shows the correlation matrix for the constructs. The diagonal elements have been replaced by the square roots of the average variance extracted. For discriminant validity to be judged adequate, these diagonal elements should be greater than the off-diagonal elements in the corresponding rows and columns. Discriminant validity appears satisfactory for all constructs indicating that each construct shares more variance with its items than it does with other constructs.

Having achieved discriminant validity the constructs in the proposed research model were deemed to be adequate.

TABLE D10: Correlation Matrix*

FO R CC CP E V

Fan Out .849

Retention .806 .849

Co-creation .532 .446 .843

Co-production .490 .233 .699 .721

Exhibitionism .057 -.163 .211 .540 .721

Voyeurism -.011 -.156 .038 .324 .678 .877

* Sqrt(AVE) on diagonal

208 Common Method Bias

Several post hoc tests were used to determine the extent to which the threat of

common method bias was present. First, using Harman’s single-factor test, all items were

entered into an unrotated principal components factor analysis to determine the number

of factors necessary to account for the variance in the variables. Accordingly, if a single

factor emerged or a single general factor explained most of the variance between the

independent and dependent variables, common method variance might be present

(Podsakoff et al., 2003). We found that a single factor explained 29.5% of the variance.

These results provide initial evidence that response bias is not a problem in the data

(Podsakoff & Organ, 1986).

Second, we used the CFA-based Harman’s single-factor test in which we hypothesized a single common methods bias (CMB) factor as causing all the indicators.

We ran the model using a smaller sample size of 300 which is “subject to more strict evaluation” (Hair et al., 2010:654) than larger samples or more complex models. The

CMB factor extracted 5.4% of the variance. A χ2 difference (χ2= 2.371;df=1, p=0.124)

test between the baseline with all the CMB paths free floating, and the CMB with all

paths equal to zero, was not significant suggesting that CMV does not appear to be a

significant source of variance in the observed items.

Structural Model

Next, we adopted structural equation modeling (SEM) for data analysis to study

the causality between model parameters using maximum likelihood estimation (MLE).

We used the clusters developed for within and between users to test our hypotheses using

the structural equation model specified in AMOS (by excluding factors related to

209 technology acceptance and technology) that we had validated in our previous study

(Fisher et al., 2011). The model’s goodness of fit measurements was good: CMIN/DF =

2.068, CFI = .994, SRMR = .0218, RMSEA = .039 (LO = .025, HI = .053), PCLOSE =

0.899.

FINDINGS

Our findings are summarized in Table D11 below. The following is an account of

each hypothesis. For the degree of voyeurism and exhibitionism between users, we found partial support. We found partial support for H1a (Z-scores = 0.58(.56), -0.70(.48), -

1.79*), the hypothesized difference for platforms with ratios of high exhibitionism and

low voyeurism between users (runways) having a greater effect on co-production from exhibitionism. The alpha for the runway SNS was greater than all other SNS groups but it was not statistically significant. We found partial support for H1b (Z-scores = 1.95*, -

0.52(.6), -0.99(.31)), the hypothesized difference for platforms with ratios of high exhibitionism and low voyeurism between users (runways) on co-creation from exhibitionism. The alpha for the runway SNS was greater than all other SNS groups but it was not statistically significant. We did not find support for H1c (Z-scores = 0.96(.33),

1.44(.14), 1.42(.15)), the hypothesized difference for platforms with ratios of high

voyeurism and low exhibitionism between users (paparazzi) having a greater negative effect on co-creation from voyeurism.

For the hypothesized effects of the ratio of voyeurism and exhibitionism within

users we found partial support. Specifically, we found partial support for H2a (Z-

scores = 0.52(.60), 0.08(.93), -1.94*), the hypothesized difference for users with high

voyeurism and high exhibitionism (engaged) having a greater effect on co-production

210 from exhibitionism. We also found partial support for H2b (Z-scores = -2.08**, -

0.21(.83), -6.77***), the hypothesized difference for users with high voyeurism and high exhibitionism (engaged) having a greater effect on co-creation from exhibitionism. We found partial support for H2c (Z-scores = 1.32(.18), -5.90***, 1.39(.16)), the hypothesized difference for platforms with high voyeurism and low exhibitionism

(voyeurs) having a greater negative effect on co-creation from voyeurism.

While the beta values in Table D11 appear to indicate that the unengaged group has greater positive effect with regards to H2a (E → CP) and H2b (E → CC) and greater negative effect with regards to H2c (V → CC), this is not the case. The imputed values for voyeurism and exhibitionism had negative values for some of the user groups. In order to see this we produced a cluster plot with exhibitionism on the x-axis, voyeurism on the y-axis, and color coding of the data points by cohort as shown in Figure D3. The negative values for all the data points in the group unengaged and most in the group moderate cause the beta values to be opposite of those for the voyeur and engaged groups.

211 FIGURE D3: Cluster Plot

212 TABLE D11: Hypotheses Summary

Hypothesis Indirect Beta Z-score (p-value) Supported Justification (non-standardized) Hypothesis compared to: H1: Between Even Runway Reserved Paparazzi Users H1a: low V and high E -0.700 between 0.578 -1.791* (.48) Beta users (.56) Runway Partially 0.396*** 0.419*** 0.384*** 0.332*** Runway Coefficients (runway) Runway vs. Supported vs. Support greater vs. Even Paparazzi Reserved positive E→CP H1b: low V -0.521 -0.995 and high E 1.947* (.60) (.31) Beta (runway) Partially 0.338*** 0.500*** 0.440*** 0.398*** Runway Runway Runway Coefficients greater Supported vs. Even vs. vs. Support positive Reserved Paparazzi E→CC H1c: high Reserved V and low 1.444 1.423 SNS 0.960 E (0.14) (0.15) participate - (0.33) Not (paparazzi) -0.183*** -0.259*** -0.106* Paparazzi Paparazzi even less in 0.246*** Paparazzi Supported greater vs. vs. UGC vs. Even negative Reserved Runway V→CC H2: Within Moderate Voyeur Unengaged Engaged Users H2a: high V and high 0.516 -1.938* E (.60) 0.079 (.93) Beta Engaged Partially (engaged) 0.394*** 0.428*** 0.644*** 0.435*** Engaged Engaged Coefficients vs. Supported greater vs. vs. Voyeur Support Unengaged positive Moderate E→CP H2b: high V and high -2.082** -6.77*** E -0.214(.83) Beta Engaged Engaged Partially (engaged) 0.556*** 0.372*** 1.646*** 0.351*** Engaged Coefficients vs. vs. Supported greater vs. Voyeur Support Moderate Unengaged positive E→CC H2c: high 1.321 V and low (.18) -5.900*** 1.385 (.16) Partially Beta E (voyeur) 0.049 -.227*** -.116** -1.663** Voyeur Voyeur vs. Voyeur vs. Supported Coefficients greater (ns) vs. Unengaged Engaged Support negative Moderate V→CC Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

Intrinsic or Extrinsic

In order to investigate whether the individual behaviors of voyeurism and exhibitionism were intrinsic characteristics within users or influenced by external factors

213 such as which SNS they were participating in we calculated the average voyeurism and

exhibitionism scores, along with their standard deviations, as shown in the Table D12

below. We found support for H3, the hypothesized driver of individual levels of voyeurism and exhibitionism. The social networks had greater variability in terms of voyeurism and exhibitionism for the same user as demonstrated by the standard

deviations. This suggests that the level of voyeuristic and exhibitionistic behavior is not

the same for an individual across networks, but rather it varies depending on which social

network they are participating in and the features it affords to engage in such behaviors.

TABLE D12: Voyeurism & Exhibitionism for Networks and Users

Group Average of Voy StdDev of Voy Average of Exhib StdDev of Exhib Social Network -0.0501 0.8379 -0.0037 0.7740 Individual User -0.0012 0.2473 -0.0280 0.2091

We also looked at the interaction of the ratio of voyeurism and exhibitionism

within user groups with the ratio of voyeurism and exhibitionism between user groups.

We did this by creating a matrix of within user ratio groups and between user ratio groups

as shown below in Table D13. We mapped the within user groups on the x-axis and the

between user groups on the y-axis. We inserted the sample size of the intersection of each

group in the center of the table. We then identified the within user groups that “matched”

the between user groups (engaged to even-handed, unengaged to reserved, and voyeur to

paparazzi) – highlighted orange in the table. We then created a sub-group within our data

set of these “matched” users and another of all the rest “unmatched” users.

214 TABLE D13: Interaction of Groups (Sample Size)

Exh M M L H Voy

M H L H Within User Moderate Voyeur Unengaged Engaged Sample Exh Voy Between User Groups 1 2 3 4 Size H H Even-handed 1 243 206 116 58 623 H L Runway 2 182 73 99 13 367 L H Paparazzi 3 120 75 49 15 259 L L Reserved 4 89 57 46 8 200 Sample Size 634 411 310 94

Using these new sub-groups (matched and unmatched) we reran our SEM

analysis. Our results are summarized in Table D14. It was demonstrated that with regards

to the impact of voyeurism on co-creation there was a significant difference between the matched and unmatched groups. In fact, the beta changes from negative for the unmatched (β=-0.239) to positive for the matched (β=3.909). With regards to the interaction of exhibitionism on co-creation and co-production there was not a significant

difference. Additionally, the R2 values on the dependent variables of Fan Out and

Retention, shown in Table D15, were significantly different between the groups – being

much larger for the matched group.

TABLE D14: Interaction Findings

Unmatched Matched Z-Score Difference V→CC -0.239 *** 0.075 (.30) 3.909*** Yes E→CC 0.409*** 0.283*** -1.508 () No E→CP 0.370*** 0.373*** 0.068 () No

TABLE D15: Interactions R2

Unmatched Matched Fan Out 0.23 0.49 Retention 0.08 0.38

215 Effect on Viral Growth

In order to validate the effect of the ratio of voyeurism and exhibitionism within

users on the viral growth factors of fan out and retention we reran a full causal model

with the results summarized in Table D16. The unengaged group of users demonstrated

much less fan out and retention than other users. The voyeur group had a much higher

likelihood for retention, suggesting that the more voyeuristic the user, the more loyal they

are to the platform. Interestingly, there is a clear pattern between individual motivation of

voyeurism and fan out regardless of the level of exhibitionism. The more frequent the

user engages in voyeurism, the greater their tendency to fan out and seek the audience of

other users to watch independent of their participation in exhibitionism.

TABLE D16: Post-hoc Analysis within Users

Voyeur Exhibitionism Label CC -> FO CC -> R (non-standardized) (non-standardized) M M Moderate .325*** .437*** H M Voyeur .419*** .601*** L L Unengaged .247*** .395*** H H Engaged .406** .412**

DISCUSSION

Voyeurism and Exhibitionism between Users

As hypothesized, our results demonstrate that the effect on co-production and co- creation from exhibitionism is greater for runway SNS where the users have low voyeurism and high exhibitionism between users (co-creation β= 0.419, p<0.001, co- production β= 0.500, p<0.001). This was argued to be the case, because our previous work had demonstrated the individual motivation of exhibitionism had a positive effect on participation in the platform processes of co-creation and co-production. Users who

216 want to exhibit their ideas, comments, pictures, and other UGC are more interested in

participating in the co-creation of value and more likely to help co-produce the product.

The SNS where users express higher exhibitionism (even-handed and runway) have more

participation in the co-creation and co-production processes. This is even more

pronounced for the runway SNS where they have low voyeurism.

The beta values confirmed the presence of greater effect on co-creation and co-

production from exhibitionism on the runway SNS than all other SNS, but there was not a

statistically significant difference to the even-handed and reserved groups. We attribute

this insignificance to the sample size of the various groups. While fairly evenly

distributed (even-handed 623, runway 367, paparazzi 259, and reserved 200) the model had 4 independent constructs and 3 control variables and the reserved group resulted in a low R2 of 0.14 on the dependent variable of retention. According to Hair et al. (2010)

with an alpha level of 0.01 this is just barely statistically significant with a power of 0.80.

While we hypothesized that the paparazzi group would have greater negative

effect on co-creation our results demonstrated that reserved group had an even greater negative effect (β= -0.259, p<0.001). This is likely, because even though the reserved had low voyeurism these were the SNSs where users were not participatory and unengaged.

An unengaged user is even more likely to not want to participate in the creation of UGC and the co-creation of value process than engaged users highly motivated by voyeurism.

Voyeurism and Exhibitionism within Users

Informed by the SET rule of reciprocity and applying the insights from our previous quantitative research, we predicted that users with balanced ratios of voyeurism and exhibitionism at higher levels will have a greater positive effect on co-creation and

217 co-production. Our results supported this hypothesis. Users who feel equanimity in their

relationships on SNS are more interested in creating UGC and thus have more

opportunities to participate in co-production – misusing the product in new and creative

ways. The beta values confirmed these hypotheses that the engaged user group had

greater positive effect on co-creation and co-production and the voyeur group had greater

negative effect on co-creation. However, these were only partially supported due to the

lack of a statistically significant difference between all the groups. We attribute this

insignificance to the sample size of the various groups. We did not have evenly

distributed groups (moderate 634, voyeur 411, unengaged 310, engaged 94). The

smallest group having only 94 data points resulted in a low R2 of 0.17 on the dependent

variable of retention and with an alpha level of 0.01 this not statistically significant with a

power of 0.80 (Hair et al., 2010).

Our results indicate that the voyeur group had the greatest negative effect on co-

creation. Voyeurs are more interested in consuming UGC rather than creating it and

therefore it makes sense that they are not interested in participating in co-creation.

Interestingly, the voyeur group had a much higher likelihood for retention, suggesting that users with a high level of voyeurism and a medium level of exhibitionism, the more loyal they are to the platform. This level of retention was greater even than with engaged users, who had high levels of voyeurism and high levels of exhibitionism. These results suggest that users who exhibit high levels of voyeurism and exhibitionism feel that they can achieve similar effects in the creation of their self-identity on other SNS. This is

probably, because they are aware of how much UGC they create and how much value

they bring to the SNS through the co-creation of content. Whereas, those users who tend

218 towards highly voyeuristic behaviors and some amount of exhibitionism feel more attached to the SNS, because they do not create as much UGC as they consume, perhaps giving them a feeling that they could not find as much UGC on other SNS. In support of this rationale, we found that the more frequently the user engages in voyeurism, the greater their tendency to fan out. Users who consume UGC are interested in getting other users to produce UGC for them to consume or to share with others (voyeurs) their experience of consuming UGC. Not only are users with high levels of voyeurism and medium levels of exhibitionism the most loyal users, they also – because of the high level of voyeurism – attempt to get other users (voyeurs) to join the SNS.

Intrinsic or Extrinsic

One question that came up during our theoretical development and subsequent analysis was whether the level of voyeurism and exhibitionism was an intrinsic characteristic – occurring regardless of the external environment – or whether these were extrinsic characteristics – changing as the situation and environment dictated. Our results demonstrate that the level of voyeuristic and exhibitionistic behavior is not the same for the same individual across networks, but rather it depends on which social network they are participating in. Some amount of the voyeur and exhibitionist traits are thus invited by features of the environments and involve extrinsic characteristics. This result prompts also the question of the joint impact of the ratio of voyeurism and exhibitionism within users to the ratio of between users. Specifically, when these ratios “match” the within user levels to the SNS between user levels, does this affect the impact of voyeurism and exhibitionism on co-creation and co-production? And, accordingly does this impact the viral growth factors of fan out and retention?

219 Our post hoc analysis of this topic reveals that, indeed, there was a difference between the impacts of voyeurism on co-creation, when the users matched the SNS.

When users and SNS matched, the impact of voyeurism on co-creation became insignificant. Additionally, the overall explanatory value of the model as demonstrated by the R2 value on the dependent variables of fan out and retention became much larger for the “matched” group. These results suggest that there are other factors that are important for users that do not match the SNS in order for them to engage.

To examine this further we retested the “matched” and “unmatched” group on the

SEM model from our previous study that included the technology adoption factors of perceived ease of use and perceived usefulness. Indeed, in this case the R2 values for both groups became quite similar (R2 “matched” fan out=0.60, retention=0.64 & “unmatched” fan out=0.52, retention=0.61).

For users who’s within ratio of voyeurism and exhibitionism does not match the

SNS ratio of voyeurism and exhibitionism between users, the traditional technology acceptance model factors of perceived ease of use and perceived usefulness become much more important. This makes sense as users that have similar or “matched” ratios will feel more comfortable with the amount of UGC being created and consumed on the particular SNS. For those users that have dissimilar or “unmatched” ratios they are more likely to continue using the SNS or recommend it based on its perceived individual level usefulness and ease of use.

However, this “unmatched” situation between the user and the SNS is likely to be transitory. The ratios of voyeurism and exhibitionism are driven by extrinsic factors and therefore we can expect that users will ‘adapt’ to a particular SNS. Alternatively, we can

220 expect that the SNS will adapt and change its features to entice ‘traits’ of the majority of

their users at a particular point in time. Thus with early adopters the SNS might start out

as low exhibitionism and high voyeurism between users as people test out the SNS,

gradually migrating towards higher levels of exhibitionism. The idea of an evolution of

the ratios between voyeurism and exhibitionism between users on SNS needs to be

further studied.

CONCLUSIONS

Implications

Our findings have several important implications. First, we have created new

typologies for categorizing voyeurism and exhibitionism between users as well as

voyeurism and exhibitionism within users. These are is important for providers of SNS to

know their market segments i.e. what type person is using these sites, why, and for what

purposes, especially, if they want to facilitate the building of social relations among

people who, for example, share interests, activities, or backgrounds.

A second implication from this study is that we have demonstrated the importance

of exhibitionism on the co-creation and co-production processes for SNS with higher levels of exhibitionism between users. While this was suggested by our previous research, in this study we demonstrated how this differs based on the ratios of voyeurism and exhibitionism between users. This implies that the co-creation of value for SNS is optimized when exhibitionism is high and little voyeurism between users. To increase their user base, platform providers should encourage users to view co-creation as a social activity, where users become active participants – showing off their skills and competence to others through UGC.

221 A third implication of this study is that the SET rule of reciprocity is highly

applicable to understand activities on a SNS, specifically within users with regard to the

balance of voyeurism and exhibitionism. Users with balanced ratios of voyeurism and

exhibitionism feel equanimity in their relationships on SNS and are more interested in

creating UGC – thus having more opportunities to participate in co-production and

misuse the product in new and creative ways. Platform developers should therefore strive

to ensure individual users feel equanimity in their consumption and creation of UGC.

A fourth implication of this study is that the ratio of voyeurism and exhibitionism

are also driven by extrinsic characteristics. These ratios can and do morph depending on

the external environment, specifically depending on which SNS that the user is

participating. Hence, design and functionality of the SNS matters in ‘inviting’ these

behaviors.

A fifth implication of this study is that when the user’s ratios within “match” the

SNS ratios between users, the growth relied more on co-creation and co-production in

driving fan out and retention. When “unmatched” the factors of ease of use and

usefulness became much more important in explaining why users continue using the SNS

and recommending it. The implication of this for platform developers is that they must

focus on ease of use and usefulness to attract and retain users if they have unmatched

conditions of exhibitionism and voyeurism.

Limitations and Future Research

The primary theoretical limitation of this research is its focus on SNS platforms and not a broader range of digital platforms. Because of this the results will be limited in their ability to be generalized across all platforms which have two sided market

222 characteristics. We may conjecture that many of the findings will be applicable to other

areas of interest such as ecommerce (Amazon), auction (E-bay), or content providers (e.g.

Yahoo) digital platforms, but we cannot provide support for these. A second limitation of this study is that when we operationalize the user behavior constructs of voyeurism and exhibitionism we adopted items from domains that are quite different from our study.

We, of course, attempted to limit respondents’ cognitive difficulties by conducting several rounds of pretesting based on Bolton (1993), but the possibility for confusion remains. An additional limitation of this research is that individuals may have been reluctant to report voyeuristic or exhibitionistic behaviors, even in an anonymous survey, due to the social stigma associated with these behaviors. Finally, the application of these drivers to the viral growth of platforms is very contextually specific and therefore limiting to the study.

Future research could also address many of the limitations by extending our social

exchange model to other platforms such as ecommerce, auction, and content providers.

This could yield additional insights into the individual motivators and platform processes

that affect the viral growth variables of fan out and retention. Additionally, future

research could look to combine factors from the two-sided economic models such as price and cost with social exchange model. This would be especially applicable to the ecommerce platforms that also have social aspects such as ratings or forums. As noted in the limitations, the application of these drivers is contextually specific and future research should look to extend the application of these beyond viral growth of platforms.

One of the most important next steps for this research is to investigate the evolutionary nature of the ratios of between users on SNS and types of features they

223 offer. It has been implied from this study that both SNS and users evolve over time with regards to the ratio of voyeurism and exhibitionism. We suspect that early adopters of

SNS start out as low exhibitionism and high voyeurism between users testing out the

SNS, gradually migrating towards higher levels of exhibitionism. Facebook started without very limited functionality for creating a profile including name, email, profile photo (limited to one), university, relationship status, etc.17 In Oct 2005, Facebook added the functionality for unlimited storage of photos with tagging of other users.18 This additional functionality encouraged the increase in exhibitionism between users. This suspected evolution of the ratios of voyeurism and exhibitionism between users along with the SNS features should be confirmed and explored further.

17 See http://www.quora.com/Facebook-Company-History/What-were-the-first-features-of-a-persons- Facebook-profile-when-the-site-launched-in-2004

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