University of Florida Thesis Or Dissertation Formatting Template

MOTIVATIONS FOR SOCIAL MEDIA ADOPTION ALONG THE PRODUCT LIFE CYCLE

DENNIS DIPASQUALE

A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

UNIVERSITY OF FLORIDA

2017

To both of my parents, for all the reasons, but especially for the support and patience as I forged a path in life different from anything either may have expected when I was born, and for the patience I suspect I inherited and that allowed me to persevere when this task became frustrating in ways it was never meant to be

ACKNOWLEDGMENTS

This document represents work well beyond the research toiled over to create it.

It should go without saying that the patience and mentorship of my advisor Dr. Amy Jo

Coffey was instrumental not only in creating this body of work, but helping me find my path as a researcher as she truly polished a rough and rusty academic. I would also like to thank Dr. Gregory Webster for sitting on an idle committee for the better part of the past five years, as well as his assistance with some of the statistical questions in forging this work. I also need to acknowledge Dr. Richard Lutz and the Marketing Department of the Warrington College of Business Administration for taking me in as one of their own and creating a new professional path that I did not expect yet absolutely relish.

Finally, to the many friends, coaches, and family for being a supporting element in my life, acting as a counterbalance to the sweat and stress involved with pursuing this path.

This achievement is not the act of one man, but the village that collected around him.

Funding for this project was made possible through grants from the JCPenney

Company and the College of Journalism and Communications. It is with gratitude that I acknowledge the support of those funds for completion of this project in a timely manner.

TABLE OF CONTENTS

page

ACKNOWLEDGMENTS ...... 4

LIST OF TABLES ...... 8

LIST OF FIGURES ...... 9

LIST OF ABBREVIATIONS ...... 10

ABSTRACT ...... 11

CHAPTER

1 INTRODUCTION ...... 12

Social Media ...... 12 Study Objectives ...... 15 Contribution ...... 15 Outline of the Dissertation ...... 16

2 BACKGROUND AND REVIEW OF LITERATURE ...... 18

Theoretical Foundations ...... 18 The Theory of Reasoned Action ...... 18 Criticisms and extensions of TRA ...... 23 Rationale for TRA over TAM ...... 25 Uses and Gratifications Approach ...... 25 Diffusion Theory and the Product Lifecycle ...... 28 Stages of the standard diffusion curve and product life cycle strategy ...... 30 PLC and adoption measurement ...... 38 Other theoretical elements to diffusion and the PLC ...... 39 Players in the word of mouth communication process, as it relates to the PLC ...... 42 Factors affecting the rate of diffusion and the shape of the adoption curve ...... 44 Perceived attributes of innovations ...... 45 Perceived risk as a factor affecting diffusion rate ...... 46 Other variables affecting adoption rate ...... 48 Criticisms of the PLC ...... 49 Social Media ...... 49 Evolution of social media ...... 50 Social media defined ...... 53 Individual differences with social media habits ...... 54 Summary ...... 56 Research Questions and Hypotheses...... 56

Method Overview ...... 61 Justification of Method ...... 62 Population of Interest ...... 64

3 STUDY 1: INNOVATIVENESS AS A MEASURE OF LIFECYCLE AND MULTIPLE DIFFUSION CURVES ...... 70

Method ...... 70 Survey Instrument and Rationale ...... 70 Reliability of Scales ...... 74 Variables ...... 74 Sample ...... 75 Results ...... 75 Pretest ...... 76 Description of the Sample ...... 78 Measurement Issues with the Product Lifecycle ...... 78 Absolute Social Media Innovativeness and Age ...... 80 Relative Innovativeness Construct ...... 81 Discussion ...... 82

4 STUDY 2: A QUALITATIVE EXPLORATION OF SOCIAL MEDIA ADOPTION ...... 86

Method ...... 86 Results ...... 87 Description of the Sample ...... 87 Social Media Use ...... 88 Gratifications Sought ...... 88 Theme 1: interpersonal relationships...... 90 Theme 2: escape/diversion ...... 91 Theme 3: personal identity ...... 92 Theme 4: surveillance ...... 92 Theme 5: curiosity/novelty ...... 93 Theme 6: social capital ...... 94 Theme 7: achievement ...... 94 Themes of non-adoption ...... 95 Discussion ...... 96

5 STUDY 3: A QUANTITATIVE EXPLORATION OF SOCIAL MEDIA ADOPTION MOTIVATIONS ACROSS THE PRODUCT LIFECYCLE ...... 101

Introduction ...... 101 Method ...... 101 Survey Instrument ...... 101 Reliability of the instrument ...... 102 Results ...... 102 Description of the Sample ...... 102 Analysis ...... 104

Factor analysis ...... 104 Variable calculations ...... 104 The causes of adoption ...... 105 Tests of hypotheses ...... 106 Discussion ...... 110

6 CONCLUSION ...... 126

General Discussion ...... 126 Social Media Adoption ...... 126 Social Media Non-Adoption ...... 128 Limitations and Future Research ...... 129 Theoretical Implications ...... 130 Uses and Gratifications and the Theory of Reasoned Action ...... 130 Diffusion of Innovations and the Product Life Cycle ...... 132 Methodological Contributions ...... 132 Adaptive Ladder Interviews ...... 132 Google Trends ...... 133 Managerial Implications ...... 133

APPENDIX

A SURVEY FOR STUDY 1 ...... 137

B SURVEY FOR STUDY 3 ...... 143

LIST OF REFERENCES ...... 149

BIOGRAPHICAL SKETCH ...... 158

LIST OF TABLES

Table page

3-1 Reliability of Scales ...... 85

3-2 Snapchat adoption by age ...... 85

3-3 Adopter Categories ...... 85

4-1 Social Media use in the Sample ...... 98

4-2 Statements identified in interviews, categorized into themes related to UGA ...... 98

5-1 Reliability of Motivation to Adopt and Social Pressure scales ...... 118

5-2 ANOVAs for motivations for the adoption of Facebook throughout the PLC ...... 118

5-3 Social Media Adoption Motivations and Pressure ...... 118

5-4 Factor Analysis...... 119

5-5 Differences in the mean values of motivation indexes in the Introduction Stage ... 121

5-6 Differences in the mean values of motivation indexes in the Growth Stage ...... 121

5-7 Differences in the mean values of motivation indexes in the Maturity Stage ...... 122

5-8 Differences in the mean values of motivation indexes in the Decline Stage ...... 122

5-9 Results of Bonferroni Post-Hoc Test of differences for ANOVAs in Table 5-2 ...... 123

6-1 Hypotheses ...... 136

LIST OF FIGURES

Figure page

2-1 Social Media adoption as explained by the Theory of Reasoned Action ...... 65

2-2 Rate of Diffusion (left) and penetration of an innovation (right)...... 65

2-3 Google Trends raw output of various terms (Google Trends, 2016) ...... 66

2-4 Homophilous communication can help facilitate diffusion of ideas in networks ...... 67

2-5 Google Trends analysis of the adoption of Myspace, Facebook...... 67

2-6 Depiction of hypotheses and questions regarding the PLC ...... 68

2-7 Depiction of hypotheses and questions regarding TRA ...... 69

2-8 Social Media Matrix (Kaplan and Haenlein, 2010) ...... 69

3-1 Facebook’s Google Trend data over a standard product lifecycle ...... 84

5-1 Social Pressure compared with reported attitude towards adoption over the life cycle ...... 112

5-2 Means plot of social pressure over PLC ...... 112

5-3 Social pressure’s components by lifecycle. Error Bars are 95% CI...... 113

5-4 Means plot of Curiosity over PLC ...... 114

5-5 Means plot of escape/diversion over PLC ...... 114

5-6 Means plot of communication over PLC ...... 115

5-7 Means plot of social capital over PLC ...... 115

5-8 Means plot of interpersonal relationships over PLC ...... 116

5-9 The Facebook product lifecycle as modeled by adoptions per year, total adoptions, Google Trends Data together with a stylized PLC ...... 117

LIST OF ABBREVIATIONS

DOI, DT Diffusion of Innovations or Diffusion Theory

FOMO Fear of Missing Out

PLC Product Life Cycle or Product Lifecycle

TAM Technology Acceptance Model

TBP Theory of Planned Behavior

TRA Theory of Reasoned Action

UGA, UGT Uses and Gratifications Theory or Approach (sentence defines context and use)

WOM Word of Mouth

Abstract of Dissertation Presented to the Graduate School of the University of Florida in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy

MOTIVATIONS FOR SOCIAL MEDIA ADOPTION ALONG THE PRODUCT LIFE CYCLE

Dennis DiPasquale

December 2017

Chair: Amy Jo Coffey Major: Mass Communication

This dissertation used a three-study sequential triangulation technique to assess the prime motivations for adopting social media. Couched in a uses and gratifications approach using the Theory of Reasoned Action, the motivations for adoption were discerned to be categorized into several discrete categories: interpersonal relationships, escape/diversion, surveillance, social capital, curiosity, and personal identity.

Additionally, this study has demonstrated that the motivations change over time in concert with the stage of life cycle.

This research contributes to the Theory of Reasoned Action by illustrating its use in determining the interplay of these gratifications while also adding to uses and gratifications research by showing its utility in discovering these motivations but also revealing new gratifications impossible to explore in traditional media use when the approach was created.

CHAPTER 1 INTRODUCTION

Social Media

Social media as we know it has older beginnings than most people may be aware. Since the inception of the Internet in the 1960s, the social nature of humanity has been asserting itself online through various evolving technologies such as bulletin boards, listservs, and UseNet groups. Fast-forwarding to more modern times, the commercialization of the Internet and Web in the 1990s lead to more advanced portals.

The evolution of Web 2.0 and 3.0 allowed users more flexibility to assert their identity.

These changes enabled social media to evolve into services such as Friendster, then later Myspace and presently Facebook. Today, mobile technology has expanded the concept of Internet-based social media beyond the early concepts of social media envisioned as disruptive even in the 2000s with the advent of mobile-only services like

Snapchat. With the ubiquity of the web and mobile connectivity, social media’s penetration into our society is nearly complete.

Indeed, social media of today has become so ubiquitous that some people who don't have certain social media services may feel ostracized from their real life social networks. As these social media services are usually free for the users, the service owners have turned to advertising to monetize their properties. This is not a new idea: newspapers have used advertising to fund themselves for centuries. The business model is nearly as old as commercial media itself.

These new technologies that enable us to connect also allow new insights into the audience not available to simpler web-based media, let alone traditional media.

Users of social media allow these services to collect their personal data and their usage

data. Services could potentially use this data to segment these users in ways traditional and non-social media cannot. While demographics and sometimes psychographic data are the most often employed in traditional and newer settings, little has been discussed about what motivates users of these media and how such motivations may factor into not just advertising targeting, but also new service development, differentiation, and competition.

So why do people use these services? What motivates people to choose to adopt and use a social network over traditional media, or choose one social media service over another? We don't fully know, empirically. The answer may lie in theoretical tracks that span both mass communications and marketing: Diffusion of Innovations (or

Diffusion Theory) and consumer preference and attitude.

The first theoretical line, Diffusion of Innovation or diffusion theory, posits that an innovation, be it an idea or knowledge such as the idea that germs cause sickness, or a technological invention like a light bulb, spreads in a population or society at a predictable rate through certain types of individuals. New innovations are adopted in the introduction stage by innovators. Early adopters pick up innovations in the growth stage.

The majority (late and early) of a population adopt during the maturity stage. Late adopters, called laggards, are those who are the last of their population to pick up an idea or invention. Graphically, adoption rate is often represented with a bell curve while the penetration is represented by an S-curve.

Marketers have picked up on this and layered a concept called the product life cycle over the diffusion curve. This product life cycle adds a product’s decline stage after maturity, a point where the population may abandon an innovation, product, or

even idea. This is unique to marketing as the idea of unadopting an innovation, called discontinuance, is not explicitly covered in diffusion literature outside of the marketing context. Marketing scientists have expanded their knowledge with the product life cycle concept. The literature explains strategies that fit each stage of the product life cycle that can help benefit the firm’s revenue, profit margins, market share, and/or overall health.

But in this diffusion cycle how does a person decide what to adopt and what to ignore? To ascertain the answer to that, this research draws on the concept of consumer preference and the Theory of Reasoned Action, which is deeply rooted in utility theory.

The Theory of Reasoned Action (TRA) suggests that a person’s attitude (and by extension expectation of utility) is based on their beliefs and evaluations of that object.

The higher one’s positive attitude is toward a behavior, the more likely the chance is that they will engage in said behavior. In the case of diffusion, this behavior would be the adoption of an innovation. This theory doesn't look at just the innovation or the behavior itself, but breaks down the cognitive algebra an individual uses in the form of a multi-attribute model. This model takes attributes and evaluations of the consequences of behaviors in order to calculate attitude.

The multitude of possible motivations could make this calculation fairly bulky. To help condense motivations into a more manageable set, it may be possible to lean on a communications approach called Uses and Gratifications to categorize common motivations. The uses and gratifications approach assumes that most media

consumption activities by an individual usually falls into one of four commonly used categories: surveillance, diversion/escape, social discussion, and personal identity.

Relating this back to social media adoption, this dissertation posits that an individual’s adoption is sourced from one of a limited number of sought gratifications.

These gratifications can be placed into a multi attribute model. It is expected that different adopters at different stages of the social media adoption cycle will identify different gratifications sought, which can be thusly used in the future to aid identification of earlier stages of an adoption cycle. This information can be used by those developing social media services as well as advertisers to have a better understanding of the media’s current and future users.

Study Objectives

The primary focus of this study is to understand why individuals adopt social media services, particularly, this study will look at the differences in those motivations over the course of the life cycle of a particular social media service. The study examines adoption through a mixed-methods, multi-study approach to understand (1) what motivations and gratifications users articulate as historical reasons for adoption and (2) how that correlates with a service’s product life cycle. A secondary goal of this research is to develop a method of identifying a product’s stage in its life cycle through traits of current adopters.

Contribution

It is expected that the results of this research will help contribute to several areas within the academic literature while helping marketing managers and advertising practitioners make better decisions.

Many researchers have echoed Elliot’s (1974) concern about the lack of true theoretical development with Uses and Gratifications. This research offers a theoretical foundation that can support this often-used approach. By adding consumer preference theory to uses and gratifications and by showing how these two approaches work together, this research can add an element of predictability to the Uses and

Gratifications Approach.

Another potential contribution comes from understanding the underlying motivations to adopt media. Media researchers and firms seem confused by shifting media consumption habits. This research will help pull the veil from that and reveal that it is not necessarily the content per se that consumers are looking for, but the gratifications sought from the media. This can aid media firms in aligning their product with the changing trends while helping advertisers comprehend who is on what media platforms or services and what may be drawing them to that, giving richer information than simple demographics can provide.

Finally, this research will also contribute to the product life cycle and diffusion theory literature. One of the challenges articulated in the literature is the difficulty in predicting where in a product’s life cycle a firm or product may be. This is especially important from a managerial perspective. This research may offer a method to measure this phenomenon.

Outline of the Dissertation

This document will elaborate next on the theory underlying the product adoption process, attitude and motivations. Two driving theories, Diffusion of Innovations and the

Theory of Reasoned Action, will be covered. The concept of the Product Life Cycle will be explained with Diffusion Theory, while Uses and Gratifications will be explored

separately. Other ancillary theories related to these concepts, such as the two- step/multi step model, homophily/heterophily, and Technology Acceptance Models will also be presented, adding to the vast knowledge base from which this dissertation will draw. This literature review will lead to a presentation of the study’s hypotheses.

Following the literature review, this study will present the mixed methods approach used to learn about motivations. Ladder interviewing was employed to gain rich, qualitative data about the sought gratifications and motivations for historical social media adoption. Surveys were used to gain reliable data to validate the interview information.

It is expected that this approach will validate the proposed hypotheses and answer the overarching research question regarding the motivations of social media adoption. While this research doesn’t expect to definitively prove adoption, it is expected that the results will generate insightful knowledge that can be developed by the study’s author over time, along with other researchers.

CHAPTER 2 BACKGROUND AND REVIEW OF LITERATURE

Theoretical Foundations

There are two areas of theory that this study will use to understand how individuals might be motivated to adopt and use social media. The first area of theory relates to the concepts of consumer preference, utility, and attitude. These are elements of the Theory of Reasoned Action (TRA) which operates as a driving theory for this study. Using this theory, this dissertation will expand on this well-established area and how it can relate to adoption of social media services. Building on TRA, this research seeks to pair uses and gratifications approach with a multi-attribute model to help categorize motivations for social media use. The second theoretical area is Diffusion of

Innovations (or Diffusion Theory): the study of how ideas, inventions, and technology spread through a society. Just as this dissertation will pair TRA with uses and gratifications via a multi-attribute model, it will also pair diffusion theory with the concept of the Product Life Cycle, a business application of diffusion of innovations. It is expected that the combination of these theories can aid in the study of motivations to adopt social media.

The Theory of Reasoned Action

The theories of consumer preference and utility offer a beautiful and simplistic way to attempt to predict consumer choice. Researchers have expanded on these theories to help understand attitude as a predictor of choice, eventually adding in a more pragmatic and realistic element of normative pressures. These normative/social pressures help to go beyond the unrealistic concept of a rational consumer to help predict behavior. Weaving this all together, the Theory of Reasoned Action (Fishbein

and Ajzen 1976) offers an excellent predictive model that will be used to explain and predict adoption behavior. This document will expand on the evolution and key elements of those theories.

Fishburn (1970), while not the progenitor of the theorems underlying economic explanations of consumer choice, explained consumer preference as a function of utility. Simply put, given a choice between two alternatives, a consumer will choose the alternative that gives the most utility: the satisfaction experienced from a given alternative. The theory assumes that an individual is rational and will choose the more preferred alternative, that this person isn’t in a state of cognitive dissonance about the preferences, that preferences between alternatives can be characterized, and that the person can identify some of the salient preferences (Fishburn, 1970). Fishburn understood that there is a difference between the reasonable economical person

(“economic man” by some) and a normal person. Indeed, he suggests that when the differences in utility are imperceptible or barely noticeable by the decision maker, states of dissonance or indifference can exist.

The literature takes the concepts of utility and attitude and expands them to a multi-attribute setting. Understanding that consumers receive multiple benefits from multiple elements of products and services (Lancaster, 1966), multi-attribute models seek to understand how those elements or attributes combine to give utility. In multiple works in the literature, multi-attribute models of utility and/or attitude seem to settle on similar equations that will be expanded on below. The dominant models in the literature are the Theory of Reasoned Action and the Adequacy Importance model, the former

offering a better assessment of the “cognitive algebra” and the latter offering slightly higher correlations (Bettman, Capon & Lutz, 1975).

The Theory of Reasoned Action is based on work by Fishbein and Ajzen (1975).

The model they created posits that there are several key variables that can be used to predict behavioral intent: Attitude towards a behavior(Ab), which consists of the beliefs about the consequences of that behavior and the evaluative criteria associated with those consequences; and the normative pressures to engage in the behavior (SN) which consist of the pressure to comply along with the motivation to comply.

Mathematically this breaks down through the following equations:

푛 Ab = ∑푖=1 푏푖푒푖 (2-1)

푛 SN = ∑푖=1 푝푖푚푖 (2-2)

BI = (Ab)w1 + (SN)w2 (2-3)

In Equation 2-1, Ab is the attitude towards the behavior comprised of the (b) beliefs about the behavior and (e) the evaluative criteria. For Equation 2-2, SN represents social norms comprised of the expectations perceived of one’s social group

(p) and the motivation to comply with those expectations (m). Finally, behavioral intention (conation, BI) is a function of attitude and the social norms. The weights attached to Ab and SN are the final components of that equation, which can vary by situation. The first part of this model on attitude has been a critical component of many multi-attribute models.

Fishbein and Ajzen (1975) operationalize attitude as a person’s favorable or unfavorable feeling about a thing, idea, concept, et cetera (“Ice cream is good/bad”).

The authors posited that attitudes are formed based on salient beliefs about that thing.

These salient beliefs, Fishbein and Ajzen continue, are limited in number and they suggest that attitude is a combination of no more than five to nine of these beliefs. The authors operationalize these beliefs as factual statements about the item in question

(“Ice cream is a sweet desert,” Ice cream is a frozen desert”). The authors’ algebra above involves categorizing how well the item embodies that belief (“very well,” “very poorly”) or how important that belief is to the person (“very important,” “not important”).

The attitude component of the model occasionally appears in the communication literature as “expectancy value models” with slightly varying terms (importance, for example, as opposed to evaluations). Fishbein and Ajzen themselves have pointed out the similarities to expanded models of consumer utility as well. Later research found few differences among many of the attitude and utility models (i.e. Luce, 1996; Mazis,

Ahtola and Kippel, 1975). A meta-analysis by Sheppard, Hartwick and Warshaw (1988) found this model to be a solid predictor of conation (behavioral intent). One particular value of this model, especially in the realm of predicting social media adoption, is the added element of using social pressures in the predictions. On a final note regarding attitude, the additive nature of this component is in concert with Lancaster’s (1966) assertion that there are many factors that can affect one’s adoption of a product or service.

The normative component (subjective norm, SN) is a differentiating feature of this model. Derived from an interpretation of Dulaney’s behavioral hypothesis, Fishbein and

Ajzen (1975) explain that it is the result of how the social environment affects behavior.

It is important to underscore that these are perceived perceptions of pressures of referents. That is, an individual may perceive pressure from people or reference groups.

These individuals or groups are important enough to the individual to motivate that person to comply. Fishbein and Ajzen continue to explain that just as there are multiple attributes that contribute to attitude, there can be multiple individuals or groups salient to the individual that can contribute to the social pressure. Just as beliefs and the evaluations of those beliefs of the consequences of adopting are a multiplicative element of attitude, social pressures are enhanced or reduced by the motivation to comply. To illustrate: the pressure to go to the doctor to evaluate a festering mole can be placed on an individual by several groups: close family, close friends, and societal pressures regarding skin cancer. The motivation to comply with friends or society may be minimal, however, an individual might feel a stronger motivation to comply with family that overrides the other social pressures and, eventually, overrides a negative attitude about visiting the dermatologist. Later research on condom use verified the impact of group and referent pressures on this behavior, despite the attitude towards wearing a condom by college age males (Albarracin, Johnson, Fishbein and

Muellerleile, 2001). Fishbein and Ajzen explain that the pressures from groups can be explicitly stated or observed, as well as from specific authorities or just individuals one holds in esteem. These concepts are not new in social influence theory. Milgram (1965) famously showed how authority can be leveraged to perform actions one might consider distasteful (an attitude evaluation). Similarly, Asch (1956) illustrated the social pressures to conform in his experiment detailing how individuals felt pressure to pick an incorrect choice. Humorous videos can be found on the internet detailing how individuals can be

“forced” to face the wrong way on an elevator or even stand up when hearing a beep.

While Fishbein and Ajzen did suggest that these social pressure elements can factor

into attitude as well, it is important to note that even behaviors with negative attitude evaluations can still be performed regardless, and thus a distinct social normative component is necessary.

Criticisms and extensions of TRA

The Theory of Reasoned Action has seen several attempts to extend and perfect. A prime example of this attempted development relates to the Theory of

Planned Behavior (TPB). Theory of planned behavior is a later extension of TRA by

Ajzen (1991). The key differentiating element of TPB is behavior control. TPB looks to broaden TRA beyond one of the assumptions and to look at situations where there is less control. For example, one might see an advertisement for a BMW. One’s attitude towards the BMW (and the purchase) may be high, and the normative pressure to purchase or own the product may likewise be high. However, if an individual does not have the resources to make the purchase, they cannot engage in the behavior. In areas of high involvement decision making with limited resources (time or money) it may make sense to use TBP. With social media adoption behavior is not bound by limited control or resources. As such this research assumes that the TRA is sufficient. As Madden,

Ellen and Ajzen (1992) assert, “By assumption, the Theory of Reasoned Action is applicable when the behavior is under volitional control” (p. 9).

Another attempt to build on the Theory of Reasoned Action might be best exemplified by Venkatesh, Morris, Davis and Davis’s (2003) study of technology adoption. The researchers attempted to combine and evaluate several utility-type models in the domain of technology acceptance. In this case, while the research was definitely of note, the application was somewhat niche and specific, focusing on workplace adoption of technology. Indeed, as later models often complicate this rather

simple idea, this researcher fully believes in Berger and Chaffee’s (1987) assertion of parsimony: the simple theory is the better theory. The authors’ Unified Theory of

Acceptance and Use of Technology was based on an earlier Technology Acceptance

Model (Venkatesh et al, 2003).

The Technology Acceptance Model was created to investigate the use of computers from the perspective of information technology deployments (Choi and

Chung, 2013; Davis 1989; Mathieson 1991). Davis found that the perceived usefulness and perceived ease of use of technology contributed towards its adoption. Davis also suggested that the ease of use may be a causal antecedent to usefulness as operationalized, which was later confirmed by Agarwal and Prasad (1999). The assertion that the “ease of use” contributes to adoption is in concert with Diffusion

Theory’s proposition that complexity hinders innovation adoption (Rogers, 2003).

Similarly, “perceived usefulness” is most in line with Roger’s proposition that rate of adoption is related to how superior the technology is over what it intends to replace

(Rogers, 2003). That is, a new phone technology will not be adopted if it is too complicated and/or not truly superior in connecting individuals with one another. It should be noted that Davis’ original operationalization of the “usefulness” construct focused specifically on how technology use would enhance job performance (Davis,

1989). Davis built on older expectancy value theories that predated Fishbein and

Ajzen’s (1975) Theory of Reasoned Action and Ajzen’s (1980) Theory of Planned

Behavior, the latter which Mathieson (1991) found to be more appropriate for other settings and to gather more specific information about adoption, even in IT/IS domains.

Agarwal and Prasad (1999) noted the lack of individual differences in the use of TAM

and added some theoretical contributions to the model, showing that individual differences can drive technology acceptance. The authors in this case focused on demographic differences as opposed to personality. Davis’s (1989) model was later expanded by Venkatesh and Davis (2000) to include several other factors that contributed to perceived usefulness.

Rationale for TRA over TAM

TAM has been used to study social media use and adoption (e.g. Choi and

Chung 2013; Kwon and Wen 2013; Lin and Lu 2011) and at first may seem like a good fit. Indeed, TAM may be appropriate for understanding the technical aspects of certain social media services. Still, while social media has technical aspects, this dissertation operationalizes social media not as technology, but as media. In comparing social media adoption to other media, this researcher suggests that it should be done in a consistent manner with a theory that can be applied to both. Technology Acceptance

Model is designed for technological hardware and rooted in the idea that it is for productivity purposes. One element of social media and uses and gratifications of media in general is the diversion/escape element. Specifically, social media adoption is not designed to aid one’s productivity, which is a key element of the TAM. Finally, one of the strengths of the Theory of Reasoned Action over TAM is the flexibility to use any attributes. This existing feature of TRA make it quite appropriate for this application. It should also be noted that TRA and TPB have much more overall theoretical development than TAM.

Uses and Gratifications Approach

If we want to understand why people adopt and use a service, we need to understand what motivates these individuals to become users. While some have

attempted to use uses and gratifications to explore social media adoption, most of the existing research on social media focuses on the individual differences of users from a psychological perspective. This research seeks to explore how motivations and the

Uses and Gratifications approach (UGA) can help categorize these motivations.

Uses and Gratifications (Katz, Blumler and Gurevitch 1974) has been used extensively to understand why individuals use various traditional and new media, including social media, other computer mediated communication forms, and general human-computer interaction. Uses and gratification proposes that there are specific gratifications people seek and receive from media use. This approach suggests that the type of media used can be an indicator of the gratifications sought (Baran and Davis,

2009). Uses and gratifications approach makes five assumptions of an audience: the user is active in the consumption of the media, the user has a choice of the gratification sought as well as media (there must be competition for the user’s attention), that the user is aware enough to self-report the gratifications sought, and that cultural significances of, as well as any moral opinions of, the media can be suspended (Katz,

Blumler and Gurevitch, 1974). This dissertation assumes that these conditions can be met with social media. The active audience is most certainly present with social media, and the original complaints by Elliott (1974) and others regarding this has no bearing on this new medium. The question of choice in social media is clearly present, as various social media services compete with each other using similar benefits. Indeed, this is at the heart of this dissertation. Through the methods used in this study, we shall determine to what degree users can report their gratifications. In traditional uses and gratifications research, there are four commonly accepted categories of gratifications:

diversion or escape, personal relationships, personal identity, and surveillance (Baran and Davis, 2009). While Ruggiero (2000) attempted to redefine the basic needs met through uses and gratifications approach, especially in interactive environments, the researcher believes that the traditional view works effectively for social media.

Uses and Gratifications has been used to understand web site and social media use. Ebersole’s (2000) early work on web sites noted the differences in why students of various ages visited various websites. Stafford, Stafford and Schkade (2004) found the traditional UGA reasons for using the Internet and suggested adding a new social gratification. Quan-Haase and Young (2010) revealed six dimensions of Facebook use by undergraduate students. Gudelunas (2012) used the approach to understand the use of social and dating services by gay men. Curras-Perez, Ruiz-Mafe and Sanz-Blas

(2014) found strong social reasons for social network use for Spanish populations.

While the Uses and Gratifications approach can help categorize and sort the motivations for use and adoption of a media, this research cannot ignore the lack of predictability echoed by past researchers. Utility, preference and the Theory of

Reasoned Action add a strong theoretical foundation to that framework in addition to bolstering Diffusion Theory. This is not a new idea. In the early 1980s researchers started to see utility and utility models as a way to add predictive theory to the Uses and

Gratifications approach (Kippax and Murray, 1980; Palmgreen and Rayburn, 1982;

Rayburn and Palmgreen, 1984). Those researchers deviated from the established expectancy value models and concept of attitude, perhaps in an effort to let UGA stand alone. This study does not suggest leaving the core models. Indeed, the literature is filled with attempts to stray from the original Fishbein model, with no real impact on

overall prediction while adding unnecessary complications. Indeed, by then Palmgreen and Rayburn (1982) acknowledged the Fishbein model as prominent. Finally,

Palmgreen and Rayburn eliminated the normative component that this study considers important.

Diffusion Theory and the Product Lifecycle

The second theory critical to this dissertation is Diffusion Theory. Marketing scientists have created an applied model on top of diffusion theory called the Product

Life Cycle (PLC). As they are very closely related, the two will be discussed together.

As an application of diffusion of innovation, the product life cycle is a measure of the per-unit adoption of a product item, form, class or service. While not a “traditional” product like a smartphone, social media still manages to follow the traditional product lifecycle. An application of this will be illustrated below. As such, in the next section, various social media services will be used to illustrate the relevance of this concept to the media. In particular, Myspace may be used more than others, as it is the only large- scale social media property to have fulfilled its full product life cycle (Cannarella and

Spechler, 2014). Following this elaboration, methods of measuring the product life cycle will be presented.

There are two types of diffusion curves for innovations discussed in the literature and shown above. One curve, (right) an S-Curve, shows the adoption over time to theoretical full penetration. This curve is more appropriate for ideas that cannot be unlearned. A second curve (left), one more appropriate for product life cycle (PLC), is a bell curve or graph (Rogers, 1976). This bell curve measures the adoption or purchase, along with discontinuance of innovations (as new products) by members of a population of individuals that make up the target market for the product (Mahajan and Muller, 1979;

Rink and Swan, 1979; Rogers, 1976). This curve can also indicate rate of adoption: slow to fast, then slow again as the last holdouts adopt. Rogers (1976, 2003) outlined that for diffusion to occur, five things must be present: an innovation or idea, adopters, a communication channel, a social system, and time. Firms looking at the product life cycle as an application of a diffusion curve can see their own products as the innovation. Generally, adopters are buyers. Communication channels vary widely and actions such as personal sales, advertising, and word of mouth contribute greatly to diffusion. Social media generally uses word-of-mouth in the earlier stages. While diffusion studies look at a social system, firms are generally only concerned with their target market, which can be niche enough to be its own social system. The diffusion curve generally starts as a stylized S-shaped curve as pioneered by Gabriel Tarde in

1903 (Rogers, 1976). Extended out beyond maturity to harvest, death, or deletion, the

PLC curve is stylized as bell-shaped as shown.

Firms can use the product life cycle as a way to predict market trends to plan product and market strategy (Bass 1969; Golder and Tellis, 2004; Levitt, 1965) as this model is generally predictive (Cox, 1967). There are four generally agreed upon stages of the standard product life cycle, though some early research proposed as many as six.

Levitt (1965) introduced them as development, growth, maturity, decline. Later the term development would fall into disuse for the term introduction (Rink and Swan, 1979) with

“development” becoming the preferred term for products not yet commercially released into the market. It may be important to note that while the vast majority of research focuses on the S-curve, there are also logarithmic and exponential curves for certain types of products (Mahajan & Muller, 1979). Indeed, Swan and Rink (1982) identified at

least six different types of curves within 10 different life cycles through the literature, varying by product form and class. An example of a non-standard PLC would be the fashion cycle, which skips the slow intro phase, starting immediately into growth, and has no maturity phase (Wasson, 1968). This tends to manifest as a parabolic curve, while grocery products tend to display as an exponential curve (Mahajan & Muller,

1979). Still, those four stages are applied to most of the curves, with the term extended or stable maturity being commonly used for stable adoption patterns. Product categories in a stable maturity are often so because of a lack of competing technology. Items that are common in a society’s daily living are often needed and enjoy this level of prosperity, at least until a new, unexpected category comes along. Bower and

Christensen (1995) referred to these types of innovations as disruptive innovations because they disrupt the way of living. Personal computing, the telephone, the automobile, and by some assessments, mobile devices, could all be considered disruptive innovations.

It should be noted that while there are some attempts to differentiate when one stage ends and another begins (sometimes referred to as diffusion thresholds), there are no clear-cut delineations between each stage of a PLC (Dhalla and Yuspeh, 1978).

This has led to some issues in formulating predictions, but has been addressed in literature focused on the growth and maturity stage, as will be explained later in this document.

Stages of the standard diffusion curve and product life cycle strategy

As stated, there are four commonly accepted stages to the standard diffusion curve and product life cycle: introduction, growth, maturity, and decline. This section will now expand on each stage, with a focus on how businesses have treated or could treat

a product or service at each stage, according to the literature. This review generally focuses on product class or form life cycle, though many of the strategy recommendations also can apply to a brand or single product.

Introduction Stage. The measurement of a product’s life starts when it is released into the market (Cox, 1967). One of the greatest values of the PLC is to understand new products (Levitt, 1965). Levitt points out that this is where the greatest value of understanding of the PLC can come into play: It is the first impressions of a product that can affect its success (Levitt, 1965). In this stage, both word-of-mouth communication and mass-media communication are important to the diffusion/adoption process (Mahajan and Muller, 1979; Rogers 1976). While word-of-mouth (WoM) is one of the most emphasized methods of communication in diffusion research, Rogers

(2003) highlights that advertising and personal sales are also key forms of communication that have facilitated the introduction of products even before WoM.

Indeed, the opinion leaders and market mavens who communicate new products are consumers of mass media (Baumgarten, 1975; Engel, Blackwell, and Kegerreis, 1969;

Feick and Price 1987; Lazarsfeld, Berelson, and Gaudet, 1944). The introduction phase of the life cycle is a point where most firms will want to create demand for a product

(Levitt, 1965). Advertising in, as well as engaging in, various public relations activities with these media can generate knowledge amongst those key initial innovators. Indeed, when a product is new, it is plagued by low sales and low awareness (Hambrick,

MacMillan, Day, 1982; Rink and Swan, 1979) and could benefit from placement in those important media. This is a time for fluctuating markets (Klepper, 1996). It is here where

Rogers’ (2003) five key attributes of an innovation’s adoption (relative advantage,

compatibility with values, complexity, trialability, and observability) are important.

Rogers defines them succinctly, and this research will expand on them below. One caution: At times firms may feel a product has plateaued and matured. Before considering a premature maturity, firms should be aware of a saddle in the diffusion curve caused by a brief hiccup or slowdown in growth before a product truly passes the threshold of the growth phase (Peres, Muller, and Mahajan, 2010).

Rogers (2003) explained that those who adopt an innovation in this early stage, called innovators, have more financial resources, are venturesome to obsession, are more cosmopolite, not averse to risk, are able to cope with uncertainty, and able to understand complex concepts easily. He also points out that they are often less respected by a social system at large.

Growth Stage. As awareness of new products increases, so too can sales, precipitating a faster rate of adoption. This marks the passing from the introduction phase to the growth phase (Rink and Swan, 1979). At this point, more competitors can enter the market and more distribution channels open up (Levitt, 1965) lowering costs and assisting the experience curve of a firm’s production. This increase in competitors can help speed up the penetration of a product form or class (Peres, Muller, and

Mahajan, 2010). Demand in the growth stage can exceed 10 percent annually

(Hambrick, MacMillan, and Day, 1982). Here, as in the introduction phase, promotion can help establish a strong market position (Cox, 1967). As previous buyers have adopted and tried a purchase, individuals learn from that and can adopt it with less perceived risk (Bass 1969; Rogers 2003). Several researchers (see Dhalla and Yuseph,

1978) suggest that a plateau at this point can give a firm a false sense that they have

entered maturity, risking a premature change in marketing strategy. During the growth phase, strategy should be focused on differentiating the product while starting to benefit from the experience curve and learning efficiencies (Anderson and Zeithaml, 1984).

This can also be the best stage to enter the market for a product class life cycle and many products enjoy better sales forecasts and perceived quality relative to entrants during other phases (Shankar, Carpenter, and Krishnamurthi, 1999). Indeed, Apple’s iPod and Facebook were entrants during their product class’s growth stage. Those two brands achieved market dominance while not being the first mover in their respective category. Depending on the model and curve, growth rate will top out when penetration hits about 37 to 50 percent of the total potential market (Mahajan and Muller, 1979) and can indicate transition to the maturity phase. Some strategists suggest that an expansion of the potential market can extend market share at this point. In this stage, the idea that there are now more individuals using certain products can increase the utility of using said items, especially of communication innovations (Peres, Muller, and

Mahajan, 2010) which can help speed diffusion of the product and the replacement of an older product. That is, the more people that used the telephone, the more the utility of having a phone increased.

Individuals who adopt in the growth stage, referred to as early adopters, are more integrated into the social system than the innovators (Rogers, 2003). Rogers continues to explain that they may be more likely to be opinion leaders and sought after by others in their social system.

At some point in the growth or maturity stage, an innovation becomes self- sustaining. Rogers (2003) suggests that a critical mass of individuals is necessary for

this to happen. Rogers explains that this becomes all the more important with technologies that require multiple parties, such as the telephone, the Internet, or social media. The critical mass threshold to adopt for each adopter type is different and

Rogers hints that as more adopt an innovation, the general social pressure to adopt increases. Indeed, Theory of Reasoned Action addresses this through the subjective norm term. It is noted that Rogers does not actually address why people adopt innovations before critical mass, specifically.

Maturity Stage. As market penetration nears saturation, growth rate slows, sales plateau and products enter the mature stage of the product life cycle (Day, 1981; Levitt,

1965; Rink and Swan, 1979). At this point, sales generally grow with the population

(Levitt, 1965). Price competition can be more intense (Levitt, 1965). At this stage firms would hope for a stable (or extended) maturity, a condition where adoption is sustained

(as contrasted with decaying maturity, which would lead to decline (Polli and Cook,

1969). Tide detergent is a good example of a product, brand, or class enjoying extended maturity through constant innovation (Day, 1981). It is often because of fluctuations in the maturity phase that some firms misinterpret the start of decline

(Dhalla and Yuspeh, 1978). By the time of maturity, most individuals in the target population should be aware of the product (Hambrick, MacMillan, and Day 1982). The competitive environment, market structure, technologies and other supporting elements are stable (Hambrick, MacMillan, and Day 1982). The strategies for this stage are generally focused on differentiation and efficiencies (Anderson and Zeithaml, 1984). It is also in the earlier part of the maturity stage that competitive shakeout occurs (Lambkin and Day 1989) as firms with lower market share will seek to depart the market and new

entrants are rare (Klepper, 1996). To aid share and revenue, Wansink and Ray (1996) suggest that advertising can increase usage frequency. Instagram, seemingly focused on that usage frequency, has been engaged in strategies that may be geared towards increasing time spent on the service. To avoid slipping to decline, products need to be managed right and marketing efforts properly applied (Andrews and Smith, 1996).

Rogers (2003) splits those who adopt during the maturity phase into early and late majority. Those who can be categorized as the early majority are not opinion leaders and may take some time to consider adopting new products or innovations

(Rogers, 2003). The late majority, he continues, may be more susceptible to social pressure to adopt innovations and have less resources and as such are more risk averse. Laggards that adopt an idea at the end of the maturity stage or in the decline stage are often more isolated, fearful of technology or innovations, are traditional, and have a long evaluation process for adoption. Rogers continues to explain that the term

“laggard” is negative and some researchers might prefer “late adopters.”

Decline Stage. A product whose market share, sales, or adoption rate has started to fall has entered the decline stage (Levitt, 1965; Rink and Swan, 1979).

Research for the Product Life Cycle as products shift from maturity to decline is light, until recently. Much of the literature in maturity focuses on how to avoid a decline and continue to manage market share. This is likely due to the strategic nature of the PLC and the idea that, once in decline, it’s over. This is a shortcoming that some authors

(e.g. Dhalla and Yuspeh, 1978) focus on: the idea that decline is a terminal illness, when it may not be. Cox (1967) noted that the decline stage (assuming no deletion) can be the longest stage of the PLC.

Two strong interrelated arguments have been put forth as to the precipitation of the decline of a product, service, class, form, or innovation. One argument is that the product no longer meets the needs of the target market (Tellis and Crawford, 1981).

Indeed, if utility is no longer significant relative to the costs and risks associated with use, it stands to reason that a consumer might no longer use or adopt it. The second argument, perhaps as a natural extension of the first, is that though needs are being met, there is a competing or substitute technology that offers more utility (Alexander

1964; Tellis and Crawford, 1981). Indeed, looking at Myspace, it wasn’t until Facebook came to the market that the service’s adoption rate fell, as illustrated in Figure 2-5 later in this document. Myspace had the same effect on Friendster: replacing the service after being positioned as a more desirable and customizable alternative to its older brother, hence offering more utility.

Early research (e.g. Levitt, 1965; Polli and Cook, 1969) suggested that the decline stage was the end of the line, and that firms should think strategically to avoid it.

Christenson, Cooper, and Kluyver (1981) suggest that products in this stage have no strategic value to the firm. Many criticisms about the PLC come from the terminal nature of the assumptions of the PLC (as in Dhalla and Yuspeh, 1978). Later research had more alternatives. Unlike idea innovations (such as boiling water in Rogers’ (2003) example), product item decline has several specific strategic recommendations: product deletion, divestment, harvesting, or rejuvenation. Product deletion is not something that should be done without thought (Alexander, 1964; Kotler, 2000). Some research considered failing products and noted that not all should be wiped from a product portfolio (i.e. Hambrick, MacMillan, and Day, 1982). Divestment, as defined by Catry

and Chevalier (1974), is a strategy wherein a firm increases price but decreases allocation of resources until the market stops purchasing the product. This is in contrast to Christenson et al. (1981) who suggested Divestment as a liquidation of a unit or product. Harvesting is an option where a firm reduces resource allocation (as in Catry and Chevalier’s (1974) definition of divestment) but not increasing the price, in hopes of recapturing some of the revenue and improving profit (Christenson et al, 1981; Kotler,

1978). It is an alternative to deletion (Alexander, 1964) where a product is still put to market, but resources are throttled or redirected elsewhere in the firm. Anderson and

Zeithaml (1984) suggest a reinvestment can revive a product. Finally, reinvestment or rejuvenation can leverage a brand name and can be better than starting over (Berenson and Jackson, 1994).

With products, especially technology products and services, it may be incumbent to mention discontinuance. Rogers (2003) defines discontinuance and segments it appropriately. Essentially, discontinuance is the abandonment of an innovation.

Innovations that have been abandoned due to a lack of satisfaction have suffered from disenchantment discontinuance. Those innovations or products that have been abandoned for products or innovations that exhibit a higher perceived relative advantage have fallen due to replacement discontinuance. Social networking’s early years may show a perfect example of replacement discontinuance. In this category of social media, Friendster was quickly replaced by Myspace, which in turn was replaced by Facebook. Fads, such as the recent app Pokémon Go, may be more prone to disenchantment discontinuance. While this research is focused on the adoption

behavior, future research may want to look at the discontinuance behavior and/or non- adoption.

PLC and adoption measurement

There are no consistent measurement metrics for the PLC in the literature. Many articles take the adoption rate, which is the unit sales or purchase rate. In the vast majority of these articles this is implied, not specifically stated. As these research pieces are linking to diffusion research, it stands to reason that the adoption rate is measured as a purchase.

The challenge of using a purchase in research related to social media is that there is no inherent purchase of the service. In fact, one could consider two levels of use for adoption: the idea that a user does or does not have a profile on the site. While this may be the actual operationalization of social media adoption, it is not without problems and an alternative to this must be considered. Monthly active users, a measure often reported by publically traded social media firms such as Facebook and

Twitter can be problematic, especially for services that are in the early stages and can

“pad” numbers without regulatory oversight.

Another alternative method of measurement would be the use of search engine trends. Google Trends has been used to track different type of diffusions. Brammer,

Smolinski, and Brilliant (2009) were able to establish a method of tracking influenza trends using the search engine’s data. These researchers were able to track flu searches with doctor’s visits with a lag of only a day. Applying this technique, Choi and

Varian (2011) were able to track market trends more relevant to the business domain, such as consumer confidence, joblessness, and even car sales, all with a small lag.

Following this, Cannarella and Spechlet (2014) were able to track the rise and fall of

Myspace and correlate it to issues and problems that the service faced while suggesting that Facebook has matured and is set for a decline. Figure 2-3 shows the raw trends for some popular social media and recent terms as of July 2016. Note that Myspace and

Facebook show a more traditional bell-like curve, while “man bun” and Pokémon Go exhibit a more “fad” style of diffusion curve, with a rapid growth to maturity. As this tool has been shown to be a good measure of adoption, this research would consider using

Google Search trends as a viable method to track current and historical use patterns to assess a particular social media’s diffusion rate and curve.

A final possibility for measurement might lie in the correlation established in the literature of adopter categories (such as innovator, early adopter) and the typical traits associated with each type of adopter. This idea will be explored with the dissertation’s hypotheses.

Other theoretical elements to diffusion and the PLC

There are many elements that contribute to the product lifecycle. Mahajan and

Muller (1979) discuss five external transfer mechanisms that affect the diffusion of innovations within the PLC scope: mass media communications, word of mouth communications, marketing actions, user experiences, and other macro and microeconomic trends. Marketing actions (such as pricing and advertising or product design/documentation) can affect the risk associated with adoption, if a price is too high and a product unknown. Word of Mouth in the marketing context involves the user experience and can accelerate or hinder adoption, especially when a first experience is bad. Mass media can take the form of publicity and advertising. These areas are an opportunity for additional theoretical contributions to a product’s lifecycle curve and adoption rate and appear to form the basis for adoption and discontinuance of

innovations in the marketing context: The Product Life Cycle. Those transfer mediums are in addition to the five intrinsic attributes that affect an individual’s adoption of a product, service, or innovation: Advantage over what the innovation is replacing, compatibility with the value system, complexity of the innovation, trialability of (the ability to test) the innovation, and how observable the innovation is. These attributes will be detailed shortly.

Word of mouth communication and the two-step-flow model. One of the key elements of diffusion theory is that an individual must pass along information to another

(Rogers, 2003). This concept was explored as a two-step flow model by Lazarsfeld,

Berelson, and Gaudet (1944) in “The People’s Choice.” In this qualitative analysis of how individuals chose candidates, it was found that individuals who weren’t sure about who to vote for would use interpersonal channels. Those opinion leaders that they sought out often received knowledge from mass media. Thus, the flow of information from media to influential to audience in two steps gave rise to this popular theory. In reality, however, the adoption of an innovation takes more than a simple word or two to communicate, and it is not as simple as selecting a candidate. With respect to advertising of products, Brooks (1957) echoed that opinion leadership is important for this type of communication, but word-of-mouth is only important for certain products and only for part of the market, depending on the product In the above example, there was a degree of homophily, or similarity particularly in preferences or personality, involved, while Rogers (2003) points out that innovations require heterophily, or differences particularly in preferences or personality, to pass from one homogeneous network to another. Grannovetter (1973) discovered that for an innovation to escape a “clique” of

individuals, bridges are necessary. These bridges are often individuals that are deviant from the cliques with which they are affiliated. Indeed, Rogers (2003) underscored that those who are deviant from the social norms of a culture are often key to diffusion, as they are exposed to different ideas and have access to opinion leaders. Granovetter

(1973) continues to show how these bridges can help with the diffusion of innovations and that those societies who have more heterophily and whose cliques have multiple bridges have faster diffusion rates. Burt (2004) expanded on this concept of bridges and posited that “structural holes” exist between homogeneous groups. With heterogeneous individuals bridging these holes, it is through these people that new ideas and innovations can flow.

Still, some elaboration on the importance of homophily and heterophily may be important to understanding its place in the theory. Even as far back as 2400 years ago, people saw that similarity was important in social contexts. Plato recognized the importance of what we would now call homophily, observing “similarity begets friendship” (Plato, 360 BCE). More recently the term has been defined to members of a social system who are more similar to each other, who are more likely to connect

(Choudhury, Sundaram, John, Seligmann and Kelliher, 2010; McPherson, Smith-Lovin and Cook, 2001; Rogers, 2003). Rogers and Bhowmik (1970) noted that homophily can facilitate communication, while heterophily can hinder communication. Building off that,

Grannovetter (1975) in his seminal piece on weak ties, observed that close social networks coagulate around similarities and that helps diffusion of ideas within a social structure. This idea was later echoed by Rogers (2003). The limited empirical evidence supports this, particularly within social networks (real or online). While McPhearson et

al. (2001) identified many conversations about homophily in the literature, they felt that little has been done to build on extant knowledge and research was somewhat scant.

Since that observation, there have been a few pieces that underscore homophily as a key element for diffusion of ideas inside a social system. Choudhury et al. (2010) recognized homophily within Twitter networks and communication. Centola (2011) noted that homophily can help diffuse both good and bad health-related behaviors.

More generally, Jackson and Lopez-Pintado (2013) empirically identified homophily as key early in the diffusion of behavior. Thus, while homophily and homophilous communication is key for within-system communication and diffusion, Grannovetter

(1973) and Burt (2004) observed that heterophily was key for information (and thus diffusion) to jump from one social system to another. It is this dance of homophily and heterophily that allows full diffusion of innovations, ideas, and knowledge.

Players in the word of mouth communication process, as it relates to the PLC

The two-step flow is more of a simplification of a larger communication process.

The process itself starts with a firm creating a product. Sales, advertising and public relations activities would be necessary to inform opinion leaders of this product (Rogers

1976). From there, as opinion leaders and market mavens are heavier consumers of mass media (Baumgarten, 1975; Brooks, 1957; Clark and Goldsmith, 2005; Feick and

Price, 1987; Lazarsfeld, Berelson, and Gaudet, 1944) and as such are more likely to consume domain-specific media (Brooks, 1957). For example, someone who’s knowledgeable about cars would be more likely to get related information from “Car and

Driver” as opposed to “Consumer Reports.” Individuals who are more open to new products (innovators) are more likely to be deviant with respect to their core group

(Rogers, 2003) but may also have ties to other groups and be a bridge for a diffusion to

spread (Grannovetter, 1973). Once innovators and opinion leaders have adopted products, the rest of the target market can be reached. In the scope of the PLC and diffusion theory that would be early adopters, the majority and laggards (Rogers, 2003).

Early adopters in technology products are more likely to be impulsive and narcissistic

(Baumgarten, 1975). It is likely that from there, the majority of the population will adopt the product and have less distinguishing characteristics.

As a final note on the two-step model and Word of Mouth communication, some may discuss electronic Word of Mouth (eWOM) communication as an element of diffusion. While true that word of mouth is often electronic, eWOM is more often cited in the literature as referring to product reviews via strangers as opposed to its traditional cousin, which is more of an interpersonal communication device, even if through certain social media interactions. As such, this dissertation follows that convention and operationalizes eWOM as online reviews, a different paradigm and not relevant for this niche discussion, while still acknowledging its importance for diffusion of products in general. Indeed, eWOM would be more of a mass media communication while traditional WOM would be interpersonal and driven by the two-step flow.

Information Cascades and Critical Mass. Many researchers have been interested in what causes innovations to become self-sustaining. Rogers (2003) addresses this issue. Bikhchandani, Hirshleifer and Welch (1992, 1998) suggested a theory of Information Cascades in which social influences can affect the take-off and adoption of fads and behaviors. These researchers suggest that communication is critical to adoption behavior and expand on the types of communications that can affect the transmission of signals to adopt (Bikhchandani et al.1998). They underscore that the

imitative behavior is seen not just in humans but animals as well. The theory identifies four key mechanisms that create conformity behavior in the literature: sanctions on deviants, positive payoffs, conformity preference, and communication (Bikhchandani et al.1992). Golder and Tellis (2004) later used these concepts to help identify adoption thresholds. Many of these concepts, such as the conformity preference, are covered in

Theory of Reasoned Action. Still, the conversation adds to the theoretical foundations and links between diffusion theory and TRA. Similarly, the concept of critical mass also concerns itself with this starting point, but specifically with interactive media (Markus,

1987). This theory of critical mass suggests an “all or nothing” adoption approach, which may be far too rigid a concept for social media services. Regardless, the concepts of homogeneity in a society being key for the spread of an adoption inside a community or social structure is again considered important (Markus, 1987).

Factors affecting the rate of diffusion and the shape of the adoption curve

Rogers (2003) summarized the literature by explaining that five factors can affect the rate of diffusion: perceived attributes of innovations, type of innovation-decision, the communication channels, the nature of the social system, and the efforts change agents make to promote adoption of innovations. Perceived risk, be it related to economic, social, or personal security is another factor that Rogers and the literature discuss. This next section will elaborate on these variables, starting with the intrinsic traits, followed by perceived risk, and a section on the other four variables.

The first set of variables Rogers (2003) elaborates on relate to the perceived attributes of innovations that can affect the rate at which a product or innovation gets adopted. These attributes are the relative advantage the item or idea has over whatever

it is intended to replace, compatibility with existing values, trialability – the ability to test the idea or item, and observability.

Perceived attributes of innovations

Superiority. An innovation has a superior relative advantage when it is perceived to be better than the innovation that it will replace. Relative advantage (or product superiority) was found to have a measurable impact on new product adoption/diffusion (Calanton and Cooper 1981; Cooper 1979; Ostlund 1974). As

Facebook vied for Myspace users, the relative advantage for Facebook was the faster service aided by a simpler, uncustomizable interface. Ironically, this custom display was the same relative advantage that helped Myspace triumph over Friendster. The advantage became the disadvantage. Indeed, the two graphs below show the Google

Trends map of the two social media, which can be interpreted to show Facebook supplanting Myspace (Google Trends, 2016). In that exemplar, total adoption is represented by the top line, which separates from Myspace adoption roughly two-thirds into the diagram. The third line that takes off later is the Facebook trend. In this example, we can see how the perceived superiority of Facebook supplanted Myspace, while the total adoption continued uninterrupted.

Compatibility. Innovations exhibit compatibility when they are in line with the values and needs of the adopters. Congruency with values was also studied by Labay and Kinnear (1981) and found to aid diffusion. While social media’s success in Asia is not in dispute, one could have argued that creating personal profiles would be incompatible with the collectivist values of many east Asian societies which could have dampened diffusion, hypothetically.

Complexity. The more difficult an innovation is to learn or use, the higher its complexity. Complexity for many diffusions also factored in as a hindering variable

(Cooper 1979; Fliegel and Kilvin, 1966; Labay and Kinnear 1981; Ostlund, 1974).

Snapchat was intentionally made to be confusing and as such, exhibited higher complexity to slow diffusion by an undesirable (older) demographic. Essentially

Snapchat was initially designed to be confusing to limit adoption by parents (Chafkin and Frier, 2016).

Trialability. Trialability refers to the ability of an innovator to test an innovation before fully adopting it. Seeds (as in the hybrid seed example) have this because a small section of a farm can be used to plant the crop. Social media might be said to have this as one can simply create an account but not adopt. Trialability was found to positively affect diffusion (Fliegel and Kilvin 1966; Ostlund 1974). Indeed, a quick

Google search shows an abundance of trade articles espousing the virtues of trials to advance early market share and sales.

Observability. Observability simply refers to how easily the successful use of the innovation can be seen. Each variable can hinder or facilitate the adoption of a product and its diffusion through a particular market and affect the shape and rate of the diffusion curve. Researchers (i.e. Fliegel and Kilvin, 1966) often use the term communicability for observability. Observability was found to aid diffusion by Fliegel and

Kilvin (1966) along with Ostlund (1974).

Perceived risk as a factor affecting diffusion rate

Perceived risk (simplified as risk from this point on) is another factor that researchers have explored. Rogers discusses risk throughout his work, but never gives it the full attention as the other variables. As a factor or variable, risk, just as complexity,

can hinder adoption behavior (Calatone and Cooper, 1981; Cooper, 1979; Feder, 1982;

Fliegel and Kilvin, 1966; Heany 1983; Ostlund, 1974). There can be several different types of risk associated with adoption. Economic or financial risk exists when innovations or new product cost a lot of money, as perceived by the adopter (Labay and

Kinnear, 1981). The high price of an electric vehicle or a new television technology may limit the purchase decision for all but those individuals with significant discretionary income. Indeed, this may explain why innovators usually have a higher income. Social risk may also exist when products who have conspicuous consumption are adopted.

Labay and Kinnear (1981) and Rogers (2003) illustrated this, while also coupling it with the intrinsic compatibility variable discussed previously. Personal Safety is another factor to consider. A recent look at issues affecting consumer adoption of self-driving cars identified safety as one of the common concerns that were articulated. Indeed, to fracture this idea further, Featherman and Pavlou (2003) identified five components of perceived risk associated with financial e-services adoption: performance, financial, privacy, psychological, and time. Ram and Sheth (1989) discuss a functional risk wherein a new product might fail or not work as promised. One could easily argue that this functional risk is simply a component of economic risk. Despite this fractured view, it is incumbent to identify if a product or innovation carries with it some form of perceived risk if one is to attempt to predict the adoption rate. With most social media, however, adoption need not be public (such as Tinder) and the social risk is as limited as a subscription to a naughty magazine where only the mailman knows for sure. As most social media costs no money, there is no financial or economic risk. The largest potential risk one may face with social media would be the loss of privacy.

Other variables affecting adoption rate

Rogers (2003) also identified several other factors that could affect adoption behavior. The communication channel or channels are addressed partially in the discussion of word of mouth and how that contributes to adoption. Persuasion is another variable. Rogers explains it to be the persuasive action of the change agent

(such as an innovator), but it could be understood that this can be done through mass media channels (such as advertising a product) or pressure to adopt from referents (as may be the case especially with technology and social media that requires more than one individual to adopt to be useful, such as the telephone or some social media services). The nature of the social systems, including norms and values, can also affect adoption rate. Indeed, one could see that social media services often change as they propagate from penetrated systems of first adopters to harder-to-reach systems of later adopters, adding features to appeal to these individuals. Finally, the innovation decision process is also discussed. The type of innovation decision can be forced, completely optional, or a collective. Forced can be via laws (such as recycling). Collective could be any type of group decision. Optional (as would exist in social media) was what Rogers focused on in his work, and he expanded the process to five stages: knowledge, persuasion, decision, implementation, and confirmation. This dissertation zooms in on the earlier stages of this process, specifically how knowledge and persuasion forms beliefs about the product to affect the decision or intention to adopt. It is within this set of stages that the theory of reasoned action is expected to help understand the decision to adopt.

Criticisms of the PLC

While the product life cycle has been proven to be a valid and useful predictor of product adoption (Bass, 1969; Dodds, 1973; Polli and Cook, 1969; Wasson, 1968) it is not without its detractors. Much of the critical literature focuses on misinterpretations or oversimplifications of the curve. These criticisms appear to take the predictive ability of the PLC more strictly than intended, as opposed to a guide (e.g. Tellis and Crawford,

1981). One problem some researchers or firms have is not collecting enough data

(Reynolds, 1968). Dhalla and Yuspeh (1978) criticize that misinterpretation but also underscore that the theory does not distinguish well the difference between form, class and brand. Despite attempts to remedy these criticisms, they are still well founded and pervasive even in this document. One criticism highlighted the focus on the demand side and the adopters, missing the supply side and marketing actions as a contributor to the diffusion (Lambkin and Day, 1989). Indeed, Lambkin and Day (1989) contributed to the understanding the limitations of the model by explaining that the economic environment along with the firm’s competition can affect diffusion and class penetration.

Grantham (1997) pointed out an issue of knowing which stage of the PLC a product or class may be in, underscoring the lack of definitive borders, an issue this dissertation will attempt to address. Some literature (see above) seems to address that, but that would make the PLC too rigid, leaning back to the other criticisms.

Social Media

Internet-based social media are not new phenomena. They have been around almost since the dawn of the Internet with bulletin board systems and UseNet. Modern social media developed as the world transitioned into the new millennium and the

Internet’s population grew. Promotion on social media grew as this new form of

communication evolved. By the early 2010s, customers had become accustomed to using and seeing advertising on social media. With social media and social media- based advertising growing steadily, it seems apparent that firms will want to use better forms of targeting than are available on traditional or even Web 1.0 types of media. As such, it may be incumbent upon marketers to have a stronger understanding of this type of medium.

This next section will detail the evolution of social media from the old Internet, to small services with niche offerings and finally to the broad, diverse field of social media today. First, some basic history will be offered, followed by an explanation of the relevance to firms. Finally, current classification schemes of social media will be presented. Following that, some of the empirical research on social media will be presented.

Evolution of social media

With the birth and explosion of social media services such as Facebook, Twitter, and Instagram, trade magazines are dedicating more coverage to these services as channels to the customer. Following that lead, firms look to connect with audiences over these new portals. Indeed, a survey by Nielsen (2014) showed that the vast majority of marketers are continuing to increase their spending on social media advertising and promotion. Spending on social media advertising grew from $6.1 billion in 2013

(Hoelzel, 2014). to over $17B in 2014 and $23.6B in 2015 (Ollison, 2016). In 2016, social media ad spending was just shy of $31B (Statista, 2017).

Social media’s roots go back to the social needs of Internet users far before the

World Wide Web or the introduction of what might be recognizable as a social networking site today. In the 1970s, Bulletin board systems (BBS) started as a new form

of social community (Curtis, 2013; Digital Trends, 2014). Usenet was a similar system invented around the same time to serve as a social connection among early Internet users (Curtis, 2013). Later in the middle of the 1980s, CompuServe (Curtis, 2013) and

America Online (AOL) (Curtis, 2013; Digital Trends, 2014) expanded the idea of online communities. AOL introduced the idea of a “profile” (Digital Trends, 2014). Still, these services were not the social media recognized today. It was in the 1990s with the advent of sites such as Classmates.com (Digital Trends, 2014) and SixDegrees.com

(boyd and Ellison, 2008) that the more familiar connection-oriented media started to evolve.

More significant and modern social networks were launched in the early 2000s, marking the start of Social Media becoming popular with the mainstream (boyd and

Ellison, 2008). Friendster was launched first in 2002 (boyd and Ellison, 2008; Curtis,

2013; Digital Trends, 2014). The site started as a dating site with the idea that that friends-of-friends might make better matches. It was also at this point that some users enjoyed creating or connecting to “Fakesters” – fake accounts centered around interests, historical figures, bands, or even real entities or brands (boyd and Ellison,

2008). These false accounts, brands, and bands may have been some of the first brand accounts with which users started to take interest and connect.

In 2003, Friendster faced technical problems due to growing demand, which allowed Myspace to position itself as an alternative (boyd and Ellison, 2008). In addition to capturing Friendster users dissatisfied with the services downtime and lag, Myspace actively courted artists who used and were subsequently removed from Friendster to promote their bands (boyd and Ellison, 2008). In doing so Myspace was the first social

media service to encourage non-personal entities to promote themselves and profit in part from having a social media profile. Indeed, by 2007 most individuals were found to become friends with brands on Myspace, especially if this relationship offered some form of reward such as free music (DiPasquale, 2008).

While Myspace was growing in popularity, Facebook started in 2004 as a

Harvard University-only social network (Kaplan and Haenlein, 2011). Facebook expanded first to other colleges, then to high schools, and finally to corporate networks and the public in 2006 (boyd and Ellison, 2008; Digital Trends, 2014). Also in 2006

Twitter launched (Curtis, 2013) as what may be the first microblog (Kaplan and

Haenlein, 2011). Facebook became a more open platform around 2007 when its API was made public (Digital Trends, 2014). Following this, a Facebook service called

Beacon would broadcast when purchases were made from various Facebook partners, such as Fandango or Blockbuster. Beacon was unpopular and as part of Facebook’s early attempts to monetize by targeting users, ended in failure (Curtis, 2013; Story and

Stone, 2007). Despite privacy issues and Facebook’s missteps, the service overtook

Myspace in 2008 as the most popular social media service (Curtis, 2013). Around this time, Google released its entry into social media called Google Plus (Stylized as

Google+) (Digital Trends, 2014). By 2009, Facebook would claim 175 million active users (Kaplan and Haenlein, 2011). The Twitter user base jumped from under half a million users in 2008 to seven million in 2009 (Jin, 2009). Pinterest had a limited launch in late 2009 and early 2010 (Carlson, 2012) and has worked well for some firms looking to generate sales. Pinterest is superior to Facebook at driving purchases (Silver,

Hayley, Tan, and Mitchell, 2012). This growth of both penetration and diversification of

social media suggests that by 2010, this type of activity had not just become mainstream, but ubiquitous. With that diversification, however, the concept of what social media “is” needed some operationalizing and classification.

Social media defined

Boyd and Ellison (2008) proposed one of the earlier definitions of social networking, before the concept ballooned out to social media:

We define social networking sites as web-based services that allow individuals to (1) construct a public or semi-public profile within a bounded system, (2) articulate a list of other users with whom they share a connection, and (3) view and traverse their connections and those made by others within the system (p211).

While useful, this definition quickly became restrictive or outdated as social networking and media expanded to non-web-based platforms such as mobile.

With social media maturing, certain categories and classifications have emerged.

Kaplan and Haenlein (2010) proposed one of the more prominent operationalizations of social media. In their work, social media was broken down into 6 subcategories based on a matrix of Social Richness (the vividness of one’s presentation) vs. Self-

Presentation (the degree to which the user provides a lot of information about themselves). Kaplan and Haenlein (2010) then elaborated on each type of social media:

Blogs, Collaborative, Social Networks, Content Communities, Virtual Second Worlds and Virtual Game Worlds. Their matrix is presented below.

The piece also helped define what each type of social media is, based on their matrix. Briefly: blogs are media that present reverse chronologically sorted entries that can detail an individual’s life (i.e. Livejournal, Twitter), social networks facilitate connections amongst the individuals (i.e. Facebook), collaborative services allow users to work together on projects (i.e. Wikipedia), content communities focus on the

generation of content such as photographs or videos (i.e. YouTube), and finally, virtual worlds encompass any service that immerses the users in an experience (i.e. Second

Life) (Kaplan and Haenlein, 2010). Note: There can be some overlap with some services. For example, Instagram, before a change in its algorithm, was focused on content creation but would have qualified as a photo blog when the information was provided in a chronological order prior to 2016.

Individual differences with social media habits

One of the more prolific areas of empirical research on social media manifests in the area of personality and individual differences. Correa, Hinsley, and Gil de Zúñiga

(2009) had one of the earlier pieces that found extroversion, openness, and emotional stability to be related to social media use, though the researchers did not specify the nature and type of social media site on which they surveyed respondents. Similarly,

Kwon and Wen (2009) found perceived orientation and perceived encouragement as well as social identity as predictors of social media use. Buffardi and Campbell (2008) found that narcissism predicted certain types of social media activity. Hargittai (2008) observed that different personality and demographic characteristics could be found to predict social media choice. Studying Italian social networking users, Pagani, Hofacker, and Goldsmith (2011) noticed innovativeness, expressiveness, self-identity, and social identity, could be used to differentiate active vs. passive social networking users while

Pai and Arnott (2013) noticed belonging, hedonism, self-esteem, and reciprocity as variables that influenced adoption habits in a Taiwanese sample. Using a uses and gratifications approach, Fullworth, Galbraith and Morris (2014) found 10 areas that influenced social media use: procrastination, ritual, freedom of expression, escapism, conformity, recreation, information exchange, experimentation, and the creation of new

connections. Studying a Malaysian population, Zolkepli and Kamarulzaman (2014) found three needs (personal/entertainment/hedonic, social/interaction, and tension release/playfulness) that drive social media adoption. Finally, examining a Dutch adolescent population, Hofstra, Corten, and Tubergen (2015) found personality differences in Facebook adoption. In regards to other factors affecting adoption and use, need for cognition negatively impacted social networking use, while innovations positively predicted use (Zhoing, Hardin & Sun 2011). Enjoyment and usefulness were also found to influence use (Lin & Lu, 2010). Gangadharbatla (2011) hypothesized six motivational factors that influenced adoption: belonging, entertainment, communication, information, commercial value, and self-expression, but no follow-up is apparent.

Innovativeness. The concept of innovativeness, as mentioned above, is central to this study as a way to understand when individuals adopt. The concept and measurement have been fleshed out over time (Midgley and Dowling 1978). One difficulty that was exposed in the early literature was the challenge of global innovativeness, which several authors have found difficult to uncover or measure. The idea of domain-specific innovativeness, however, branched from the concept of opinion leadership. Gatignon and Robertson (1985) trace this path by understanding that opinion leadership cannot be generalized and generally this trait can vary by product category. They proposed that innovators be innovators within a product category due to their knowledge of that product category. They continue to posit that this increased knowledge allows less cognitive processing of new innovations and proposed that this lower involvement contributes to faster adoption of new products. This is in line with research by Midgely and Dowling (1978) in which the innovativeness trait is comprised

in part by interest in a product category. Goldsmith and Hofacker (1991) took these principles, understanding the difficulty of measuring global and/or innate innovativeness, and worked to create scales to measure domain-specific innovativeness.

A discussion not found in the literature is whether innovativeness can be relative and/or absolute. Currently, the literature discusses innovativeness as a singular trait, presumably within the context of the larger society. The literature does not currently address whether one can be very innovative within their peer group (relative innovativeness), yet not innovative within society at large (absolute innovativeness).

Summary

The above literature review provided an outline of the extant knowledge on key elements of this study. Theory of Reasoned Action shows how the attitude towards the consequences of an adoption behavior can help predict behavior activity. Uses and

Gratifications approach helps us understand what people seek to gain by the adoption and use of various media. Parallel to TRA and Uses and Gratifications, Diffusion Theory helps us understand how innovations and information propagate throughout social systems. Attached to Diffusion Theory, the concept of the product life cycle has been used as an application of Diffusion Theory to understand consumer adoption of products. Altogether, the theoretical journey provided above lead to some expectations in the domain of understanding the adoption of social media services over time. These expectations and questions will be explained in the next section.

Research Questions and Hypotheses

This research is primarily concerned with understanding the primary motivations involved in an individual’s decision to adopt modern internet based social media services (social media), and how that changes over the life of a particular social media

service. Because of the lack of refinement of life cycle measurement, additional propositions arise to add to the literature another method of PLC measurement. To that end, two themes exist within this study that need addressing: understanding motivations to adopt social media services, and better product life cycle measurement. This next section will elaborate the logic involved with these themes, and propose research questions and hypotheses that will direct the methods that will be used to study these phenomena. Though motivations to adopt is the driving theme, measurement will be addressed first.

The primary objective of this study is to answer overarching research questions:

 RQ1: Why do individuals adopt social media?

 RQ2: How do the motivations to adopt social media change over the product life cycle of said social media?

To study a social media services adoption in the context of a product life cycle, it may be helpful to understand how some diffusion curves can evolve. In conceptualizing when a service may be in a particular PLC stage, a concern arose that a social media service may have at least two diffusion curves, one for its core, root, or relative social system, and one for a larger parent or absolute social system that contains the smaller sub group, the relative social system. To illustrate this issue, consider a hypothetical illustration of Snapchat. As of mid to late 2016, this social media service may have significantly penetrated the under-25 market, while it may have only slight penetration in the 40 to 60-year-old demographic. The former suggests that Snapchat is in maturity for the younger demographic while simultaneously in the introduction stage for the older demographic. Taking the population as a whole, the service may be in the growth stage, hypothetically. As the literature has established that an adopter has certain traits

inherent to them depending on the stage when they are adopting, it follows logically that the stage in a diffusion cycle can be determined by the primary or modal adopter at the time. Additionally, it also follows that as an innovation starts spanning bridges to new sub groups, multiple adoption curves may exist. Therefore, this research makes the proposition that a social media service may have multiple diffusion curves relative to the target demographic or demographics, and the population as a whole. This study makes an additional proposition that these curves can be measured by understanding the type of individual adopting the product at a given time, as measured by innovativeness.

These propositions lead to the following hypotheses:

 H1: The current stage of diffusion (or product life cycle) will correlate with the innovativeness score of the modal adopter of the social system of measurement.

 H2: An innovation (or product) that has established itself in multiple social groups will be in two or more stages of the PLC.

As much of the literature asserts that those who adopt earlier are younger, it would follow that age and innovativeness will be correlated. It should follow that this phenomenon would be represented with innovativeness scores. Therefore, the following hypothesis is presented:

 H3: Absolute Social Media Innovativeness will be correlated with age such that younger individuals will exhibit more innovation.

Additionally, it was expected, based on the literature, that innovativeness was a more dynamic proposal such that one can exhibit innovativeness within a smaller social structure such as a small group of friends but not compared to society as a whole. While some individuals may exhibit the same score on both groups, not all would and some differences should show. As such, the following is proposed:

 RQ3: Can individuals exhibit a score of innovativeness relative to their immediate social structure (Relative Social Media Innovativeness, SMIrel) that differs from their innovativeness score relative to society as a whole (SMIabs)?

The driving theme of this dissertation is to understand the motivations of those who adopt a social media service. Uses and gratification approach proposes that gratifications sought through media consumption can be distilled into relatively few categories. The Theory of Reasoned Action posits that, basically, behavioral intent follows attitude towards that behavioral intent and that the attitude is a component of the beliefs and evaluations of the consequences of those behaviors. This research asserts that those consequences can be distilled into categories of gratifications sought, and that they can be evaluated with Theory of Reasoned Action to predict behavior

To address the question of motivations and the categorizations of those motivations, this dissertation asks the following.

 RQ4a: Into what categories can gratifications sought (as motivations to adopt) be categorized?

 RQ4b: How do gratifications sought contribute towards the attitude towards the behavior of adopting social media?

The Theory of Reasoned Action also posits that normative influence can affect the behavioral intent, in this case to adopt. Similarly, Diffusion Theory suggests that as more people in a social system adopt an innovation, that the adoption rate will speed up. This leads to our next hypothesis:

 H4: The social normative term will exert a greater influence later in the product life cycle while attitude will show less influence

An implication of this hypothesis is that someone may feel forced to adopt a service, even when they dislike the idea of adopting it. A scenario can exist such that an individual considers the consequences of signing up for Facebook as negative, but feels

left out of the social system as a result, and therefore eventually complies with the pressures of that social system.

The literature also suggests an additional phenomenon. As the literature has established that adopters vary by stage, it makes sense that motivations should also vary by stage. This leads to the next assertion:

 H5: The primary gratification sought by a social media service will vary by life cycle stage.

This follows in product categories as well. As a product category grows and enters the growth stage, this is when a category begins to differentiate allowing competitors to enter the market to handle the overall heterogeneous market by offering different products to service different consumers in different subgroups.

Additionally, as a test of the Theory of Reasoned Action and the proposed models, this study has an opportunity to verify that theory. While a one-to-one correlation is not expected, due to the self-report nature, some agreement should be found. As such H7 is proposed:

 H6: Calculated attitude towards social media adoption will correlate with reported attitude of social media adoption.

Within Diffusion Theory there is a concept, critical mass, which posits that the more individuals within a social structure that have adopted an innovation, the more likely others in that same structure will also adopt the innovation (Rogers, 2003). This is especially true with technologies such as the telephone that require others to adopt it to gain utility; this research proposes that the subjective normative component will be related to the number of referents in one’s social system that have adopted a social media service. As it might be expected that a social group can vary in size for any number of reasons, this research suggests that it would be more of a function of a

percentage of adopters as opposed to a concrete number. As such, the following is proposed:

 H7: The subjective normative component will be positively correlated with the percentage of one’s referents who have also adopted a social media service.

What cannot be discerned from the literature, however, and in a similar vein to

H7 is whether the number or size of one’s referent group would affect the pressure to adopt, the motivation to comply, or both. As such, the following research question is proposed:

 RQ5: How does the number of one’s referents contribute to the subjective norm component of the TRA model?

Finally, it may also be of interest to know why users have chosen not to adopt a chosen social media. Through the methods to follow, this study hopes to learn some insights as to what influenced the non-adoption (having never adopted the media), or the discontinuance of certain social media. As such, the following exploratory research question is posed:

 RQ6: Why do some individuals choose not to adopt social media?

Method Overview

Pursuant to de Vaus (1990) good explanations of a phenomenon come in two stages, the first being the grounded theory, which this study established with a thorough review of literature. The second comes in testing the theory and hypotheses. This establishes falsifiability and is in line with one of the key criteria of a good theory as posited by Berger and Chaffee (1987).

This dissertation used a mixed-methods approach to test the stated hypotheses and answer the research questions. While most of this research is housed in a quantitative approach, qualitative interviews were used to gather rich data for analysis

and to develop the final study in this dissertation. This introduction will detail the overarching goals, explain the author’s research philosophy, and explain the justifications for the use of a mixed methods approach before presenting a brief of the three studies this dissertation conducted to address the theme of the document.

Justification of Method

The goal of this study was to understand consumer motivations during various adoption stages of social media. The author of this dissertation asserts that this is a concise topic of study, which is necessary for good methodological design (Bordens and

Abbot, 2011). This study used a mixed-methods approach with qualitative interviews and quantitative surveys. This two-step approach is consistent with the approach used in attitude research as well as for the model this study used (Ajzen and Fishbein 1980;

Fishbein and Ajzen, 1975). Indeed, extensive support and validation exists for this type of mixed approach.

The author of this dissertation believes in a post-positivist philosophy of research.

This philosophy demands an empirical observation as unbiased as possible (Baran and

Davis 2009). This principle will guide even qualitative methodologies in a mixed- methods approach. The researcher believes that a mixed approach will bring hard, generalizable data with rich qualitative data that can offer more detail and insight. The author of this study is in agreement with Johnson and Onwuegbuzie (2004) that the mixed approach can capitalize on the strengths of each mono-method design while helping to curtail the limitations of both and “offer the best chance of answering their specific research questions” (p15).

Mixed methods research was formally defined in Johnson and Onwuegbuzie

(2004) as “the class of research where the researcher mixes or combines quantitative

and qualitative research techniques, methods, approaches, concepts or language into a single study” (p17). In a later analysis of the contemporary opinions of prolific mixed methods researchers of the time, Johnson, Onwuegbuzie, and Turner (2007) underscored that a critical element to mixed method is the pairing of qualitative and quantitative methods. That is indeed what this dissertation did with the use of laddering interviews (a qualitative method) to start, leading to a survey-type instrument and analysis (quantitative). The major justification of this research is the complementary element of this philosophy that takes the strengths of each to minimize the weaknesses of the other (Johnson and Onwuegbuzie, 2004). Basically, a mixed methods approach is a way to combine the best of qualitative and quantitative methods (Harwell, 2011).

Indeed, this study follows one of the exemplars: interview then generalize with a survey.

This was used to illustrate and justify a mixed methods approach advocated by Johnson and Onwuegbuzie (2004) and includes an element of creativity in research design to discover the best way to find answers to research questions as to not be limited by suggestions of the past.

This dissertation used a sequential triangulation mixed methods approach

(Johnson, Onwuegbuzie and Turner, 2007) with in-depth interviews that used a laddering technique which then guided subsequent survey design. Ladder interviews involve a set of probing questions designed to root out hidden motivations. The theoretical derivation of questions leading to the design is in line with a bottom-up approach. Given the quantitative focus of the study, this dissertation could be labeled as

“Quantitative Mixed” or “Quantitative Dominant” (Johnson, Onwuegbuzie & Turner,

2007).

Population of Interest

This dissertation is focused on American adults. Many studies in marketing and psychology-related disciplines use a convenience sample of undergraduate students to understand a national sample. While one can find a defensible argument for some generalizability with an undergraduate sample, the expectation of age and generational differences demanded a more heterogeneous sample for key parts of the study.

The data collection and analysis of this dissertation spanned three studies. The first study addressed measurement issues, tested for multiple diffusion curves, and explored the concepts of absolute and relative innovativeness. This first study used a survey instrument with a quantitative focus distributed to a national sample. The second study was a qualitative exploration of the gratifications sought from social media services via ladder (or laddering) interviews. The third and final study built on the first two studies to create a survey instrument that determined, primarily, how uses and gratifications contribute to the attitude towards adoption behavior. This third study also tested social influence throughout the life cycle.

The next three chapters will expand on each study. Each chapter will explain the method used, present results, and discuss the results both in general and how they contribute to subsequent studies in this dissertation. Following this, a final chapter will examine the study as a whole, concluding and discussing implications for both managers and researchers.

Figure 2-1. Social Media adoption as explained by the Theory of Reasoned Action

Figure 2-2. Rate of Diffusion (left) and penetration of an innovation (right).

m un ace ra p g a n B n t yS a s n M M I o t k a o h n G n o o b pC a ém ace n k F S o P

Figure 2-3. Google Trends raw output of various terms (Google Trends, 2016)

Figure 2-4. Homophilous communication can help facilitate diffusion of ideas in network A or B, but heterophily is necessary for an innovation to travel through deviant, heterophilous connections. In this case, “C” is a heterophilous connection between networks “B” and “C” through which an innovation may travel.

Figure 2-5. Google Trends (2016) analysis of the adoption of Myspace (blue), Facebook (orange), and the two combined (grey) as a category of social networking within social media. This illustrates a relatively stable trend of social networking, while Facebook’s relative advantage helped it surpass Myspace. Trend data is from 2004 to 2011.

Figure 2-6. Depiction of hypotheses and questions regarding the PLC. Lines shown are approximations and not meant to imply values

Figure 2-7. Depiction of hypotheses and questions regarding TRA

Figure 2-8. Social Media Matrix (Kaplan and Haenlein, 2010)

CHAPTER 3 STUDY 1: INNOVATIVENESS AS A MEASURE OF LIFECYCLE AND MULTIPLE DIFFUSION CURVES

Method

Survey Instrument and Rationale

To address hypothesis 1: measurement issues, along with hypothesis 2: multiple diffusion curves, this study used a survey instrument (Appendix A). This instrument assessed innovativeness of individuals against their self-reported adoption time of

Facebook, Snapchat, Twitter, and Instagram. Subjects were then directed to their

Facebook and Twitter profiles and instructed how to determine when they joined. A survey assessing the adoption time of Facebook is appropriate as the service has essentially reached a maximum penetration of the US market as of 2016 (Advertising

Age, 2016). A second social media service, Snapchat, has not reached maximum penetration, but has hit a significant penetration of some demographics with minimal penetration of others (Advertising Age, 2016). As such, Snapchat seems like the best medium to assess multiple lifecycles as per H2.

As this dissertation is interested in social media adoption, the instruments measured innovativeness along the domain specific trait of social media. Midgley and

Dowling (1978) discussed measurement issues regarding innovativeness and noted that some consumers may score higher on innovativeness scales for certain products and lower for others. This domain-specific measurement was addressed specifically by

Goldsmith and Hofacker (1991). To measure absolute social media innovativeness, the researcher chose Goldsmith and Hofacker’s (1991) adaptive scale. This scale was designed to measure domain-specific innovativeness traits and was tested against several domains including fashion and music. The original scale had acceptable

reliability (Cronbach’s alphas of 0.83 to 0.85 for various domains) and “compelling” validity (Bentler-Bonett score = 0.978). While several other scales were available (see

Roehrich, 2004), the intrinsic adaptability of the Goldsmith and Hofacker scale makes it ideal for comparisons between domain specific applications relevant to relative innovativeness score as well as the absolute innovativeness score. This scale consists of six items. To condition the subjects to the proper frame of mind, the statement “For the next set of questions, think about how you might compare to your primary social group” was added for relative innovativeness, while “For the next set of questions, think about how you might measure relative to society as a whole” to measure absolute innovativeness. The items in the original research are as follows:

1. Compared to my friends, I own few rock albums

2. In general, I am the last in my circle of friends to know the titles of the latest rock albums

3. In general, I am among the first in my circle of friends to buy a new rock album when it appears

4. If I heard that a new rock album was available in the store, I would be interested enough to buy it

5. I will buy a new rock album, even if I haven't heard it yet

6. I know the names of new rock acts before other people do

Adapting the scales social media, this study used the following altered items to assess relative innovativeness within the domain of social media:

1. Compared to my friends, I am a member of fewer social media services

2. In general, I am the last in my circle of friends to know about new social media services.

3. In general, I am among the first in my circle of friends to adopt a new social media service when it appears

4. If I heard about a new social media service that was available, I would be interested enough to try it

5. I will try a new social media service, even if none of my friends have tried it yet

6. I know about new social media services before other people do

Item #5 is the most altered for this particular application. As items 4 and 6 may measure global or general innovativeness, they would not be duplicated with the next scales. The next six items are to measure absolute innovativeness. The modifications change “my friends” and similar phrases to refer to the wider population.

7. Compared to the rest of society at large, I am a member of fewer social media services

8. In general, I am the last person to know about new social media services

9. In general, I am among the first people to adopt a new social media service when it appears

10. If I heard about a new social media service that was available, I would be interested enough to try it

11. I will try a new social media service, even if nobody else is using it

12. I know about new social media services before other people do

To measure multiple adoptions of various services, the following survey items were used:

For the following questions, “adoption” refers to regular use: that is creating an account, customizing a profile, and actively using the service at least once a week to post or check on other’s updates.

13. To the best of your knowledge, when did you adopt Facebook (early/mid/late) Year: ______or Never

14. When you adopted Facebook, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last)

15. To the best of your knowledge, when did you adopt Instagram (early/mid/late) Year: ______or Never

16. When you adopted Instagram, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last)

17. To the best of your knowledge, when did you adopt Snapchat (early/mid/late) Year: ______or Never

18. When you adopted Snapchat, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last)

19. To the best of your knowledge, when did you adopt LinkedIn (early/mid/late) Year: ______or Never

20. When you adopted LinkedIn, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last)

21. To the best of your knowledge, when did you adopt Twitter (early/mid/late) Year: ______or Never

22. When you adopted Twitter, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last)

For these items, social media were chosen from Statista’s (2016) list of most popular services. Though the focus of this dissertation is on synchronous services that require both parties to participate in a connection, asynchronous Twitter and Instagram were added for potential post-hoc explorations or comparisons. Also, despite this dissertation being focused on generalized social media, domain specific service

LinkedIn was added for the same post-hoc exploration. As the adapted scales and the adoption questions were different and/or new, a small pretest was conducted with a convenience sample before adjusting and distributing the survey.

The survey is the best method for this type of research. Besides using established scales, the self-report of actual historical quantitative data cannot be gathered with any other method available to the researcher. Beyond that, de Vaus

(1990) discussed how surveys are one of the most used research techniques, and a sound method of determining natural variation in a population. The introduction of

variability through artificial means as with experiment was not conducive to this study.

Qualitative methods may yield richer data but are prohibitive in reaching the number of respondents necessary for proper statistical analysis.

Reliability of Scales

Cronbach’s alpha was calculated to assess reliability of the scales for this study

(see Table 3-1). Though questions on group technology use and group social media innovativeness were created more as a prime, they had reasonable reliability as a measure. The modified innovativeness scale for the group innovativeness was also acceptable. Scales for Relative Social Media Innovativeness (SMIrel) and Absolute

Social Media Innovativeness (SMIabs) were also within an acceptable range and closely matched the original scale’s reliability of 0.83 to 0.85.

Scores for the scaling questions were calculated in concert with Hofacker and

Goldsmith (1991). The score for the group’s technology innovativeness were based on three rather than six questions. To keep the size range consistent with other scores, the technology innovativeness score was doubled (weighted).

Variables

This study attempted to measure users’ relative and absolute innovativeness within the domain of social media along with the adoption time of a variety of social media services. While the focus of H2 analysis was on Snapchat, the use of other social media services was used for other for post-hoc analysis. Core to the analysis of this first study were the variables of absolute social media innovativeness, relative social media innovativeness, adoption time of Facebook, adoption time of Snapchat, and the socio- demographic characteristics of age, gender, education, income, family income, and location.

Sample

This study used a national sample to ensure age diversity. A convenience sample of college students might have been too homogeneous given the research questions in this study. As a national sample is more likely to be heterogeneous, better information can be used for analysis and conclusions regarding how heterogeneity can create several diffusion curves. Irish (1992) presents several arguments regarding sample size. These show a diminishing return on larger samples. In concert with Irish’s recommendations, this study looked for a final sample size of 400. Similarly, de Vaus

(1990) would be in agreement with their numbers.

Panel Recruitment. Survey respondents were recruited via InnovateMR

(Innovate) panels and compensated $1.25 in points in accordance with Innovate policy.

Innovate recruits panelists using a proprietary method. Respondents are emailed with a link to participate, answer a few question to check attention, and are given a link to the

Qualtrics survey used in this research. A sample of individuals greater than the target number are contacted with the email link. If targeted numbers are not met after a period of time, additional potential subjects are sent the link. The survey was restricted to computer entry, to ensure users have the ability to view their Facebook and Twitter profiles. Using a mobile device for survey completion may have made viewing

Facebook and Twitter profiles more challenging or confusing for a segment of the survey where users were asked to view their profiles.

Results

This section will detail the results of the first study and how it relates to the first three hypotheses. First, a brief explanation of the results of a pretest will precede a description of the final sample used. Next, results (calculated in IBM’s SPSS (v19)

software) will be detailed as they relate to the first three hypotheses. Following those results will be a brief discussion of some of the limitations and implications surrounding the first study.

Study 1 was designed to test three hypotheses:

 H1: The current stage of diffusion (PLC) will correlate with the innovativeness score of the modal adopter of that system.

 H2: An innovation (or product) that has established itself in multiple social groups will be in two or more stages of the PLC.

 H3: Innovativeness will be correlated with age such that younger individuals will exhibit more innovation.

Hypothesis 1 posited that by studying social media, the “location” of a product along the diffusion curve of the product life cycle could be determined by looking at the modal type of adopter at any given time. Hypothesis 2 proposed the concept of multiple product life cycles or diffusion curves or states, in which a product could, hypothetically, be in the growth stage for one social group and the introduction stage for another.

Hypothesis 3 stated that absolute social media innovativeness will be related to age, in concert with the literature.

Pretest

A pre-test of the survey was completed by 57 individuals. This pre-test revealed a potential that subjects would not understand the difference between the absolute and relative scale questions for innovativeness. As the similar questions may not have distinguished themselves enough, priming questions were added to the survey to aid in the subject’s consideration of absolute and relative scales. Additionally, this required some extra operationalization of the terms for Absolute and Relative Innovativeness as

is relevant to this study. Before creating these questions, these constructs were more carefully defined as such:

Absolute Innovativeness is the innovativeness score relative to a wider society or social structure. Absolute Social Media Innovativeness, for the purpose of this study, is the social media innovativeness score of an individual as it relates to the population of the United States.

Relative Innovativeness is the innovativeness score relative to a subset of the larger social group. Relative Social Media Innovativeness, for the purpose of this study, is the social media innovativeness score of an individual as it relates to a subset of individuals considered to be their primary social group.

To aid in the priming of one’s social group, an open-ended question was used to ask participants to briefly describe their social group. Secondly, questions were asked to discuss how technologically innovative their social group was relative the US population. Finally, the Hofacker and Goldsmith (1991) scale was again adapted to assess the perceived social media innovativeness of their primary social group. Finally, randomization was added to the scales while the order of absolute and relative blocks was also randomized to avoid order-of-effects. Due to financial constraints, the new survey was not re-tested.

The final survey was sent to a panel of 2,068 potential subjects by InnovateMR until the final 400-member sample was reached for the first study’s total allotted sample.

To reiterate: when quota is not reached, subsequent sampling is done until the system reaches the assigned number. Some quality control measures for data cleaning were employed. It was determined that the researcher could take this survey in 210 seconds.

As such, respondents who took less than 200 seconds were removed as they likely did not read the survey properly or otherwise rushed through the instrument. Next, subjects who entered either no information or gibberish responses to the open-ended priming

question were also removed. Finally, because two questions were reverse-coded, answers were scanned for irregularities in such a manner that subjects whose responses to reversed survey questions matched non-reversed questions (all 7s, 6s, 2s, and 1s) were also removed. If in doubt, the subject was retained to avoid selection bias.

This left a final n of 237 respondents out of the 343 completed surveys for study 1.

Description of the Sample

The mean age of the respondents was 41 years old (SD = 17.6), in a range of 17 to 78. There were slightly more female participants (55.3%) than male participants

(43.9%). A quarter of the sample had no college education (2.5% had not finished high school, 24.1% graduated high school but did not go to college). The income of the sample was spread evenly amongst those earning under 75,000 dollars annually. Most of the sample had been or were still married, with 38.8% of the sample indicating they were single or never married. Nearly half of the sample indicated that they have no children (45.0%) and 46.6% of the sample indicated they were not employed, compared to 18.2% indicating part-time employment and 31.8% indicating full-time employment.

The high level of unemployment in the sample could be indicative of the online panel. A well-represented majority (78.6%) were not enrolled in any type of schooling.

Measurement Issues with the Product Lifecycle

Before addressing the hypotheses, Google Trends data was used to analyze and validate this tool as one method of determining product life cycle stage and thresholds.

Based on Google Trends data overlaid on a standard product lifecycle map (Figure 3-1), thresholds and phases for Facebook were estimated as follows: Introduction 2006 to

2008; Growth 2008 to 2010; Maturity 1, 2010 to 2012; Maturity 2 2012 to 2014; Decline

2015 to present. As thresholds can never be truly specific, middle years are taken as a

solid indicator as to the stage such that 2007 or earlier was in the introductory stage,

2009 was the growth stage, 2011 to 2013 was the maturity stage, with the peak at 2012, and 2015 onwards was coded as the decline stage. Essentially, threshold years were avoided as they could be ambiguous. An analysis of means showed trends in the number of adopters, as would be expected for a product, but did not show any significant differences in the absolute innovativeness scores. As current innovativeness may not be indicative of prior innovativeness, especially considering age as a factor, this is not surprising. The trends in the number of adopters is indicative of a standard S- curve of adopters as would be expected, with a smaller number of adopters in the introduction stage (2007, 9 adopters), a large number of adopters in the growth stage

(23) and an easing of adopters in the maturity stages (16, 9 and 11 for 2011, 2012 and

2013). This is enough information to validate the use of Google Trends data to support its use for product life cycle analysis.

As Snapchat is a newer service, the curve is harder to interpret. Snapchat’s trend curve shows two possibilities: maturity (and perhaps decline), or that it may have entered a pre-growth “saddle” as Peres, Muller, and Mahajan (2010) noted. If Snapchat has entered a saddle, it should be entering its growth state soon. If that’s the case, it could be in two or more states of diffusion. Ages for the sample were grouped and the penetration was analyzed. An ANOVA indicated a significant difference amongst the groups (F(5, 223) = 20.221, p < 0.001). The different penetrations are indicated in

Table 3-3.

Comparing the youngest demographic (as a social group) to others, a high penetration indicative to entering a maturity phase is apparent. The data indicates lower

penetration of other age demographics. The data also shows the median time of adoption changes such that older adopters adopted later.

Pursuant to Rogers (2003), individuals within one standard deviation of mean innovativeness were scored as early or late majority. These scores would be 15.1 to 31.

Individuals who scored 0 to 15 were assigned to the laggard category. The frequencies of innovators, early adopters, early majority, late majority, and laggards closely matched

Rogers’ (2003) idealized expectations (see Table 3-3).

In analyzing Snapchat adoption year by scores, little can be discerned from the small sample of adopters who indicated their year of adoption. A visual analysis of the data shows the potential for support for H1: Innovativeness score differs with stage of diffusion, but the relationship was not significant (X2(24, N=49) = 20.357, p=0.676). As such, this study cannot support H1: measurement of current stage by modal adopter.

However, given the indication of high penetration of one social group vs. low penetration of other social groups, H2 is supported: an innovation (or product) that has established itself in multiple social groups will be in two or more stages of diffusion.

Absolute Social Media Innovativeness and Age

Hypothesis 3 predicted a negative relationship between age and absolute social media innovativeness (SMIabs) such that SMIabs declines as subjects age. Simple linear regression was used to test this relationship between absolute social media innovativeness and a subject’s age. A significant relationship was found such that age was negatively related to innovativeness; as a person gets older, SMIabs scores dropped, as was predicted by H3 ( = – 0.258, t = – 4.015, p < .001). Thus, H3 was supported such that age can predict innovativeness. While a statistically significant

result, only 6% of the variance was explained (R2 = 0.066, F(1,227) = 16.124, p <

.001). Additionally, all other demographic relationships with SMI were non-significant except employment and marital status. However, when controlling for age, these variables did not show significance. Therefore, only age serves as a predictor variable for SMIabs with this dataset.

Relative Innovativeness Construct

This dissertation also sought to establish a construct for relative innovativeness.

Relative Innovativeness refers to how innovative a person is, relative to a particular subculture or group, in this case respondents’ primary group of friends. This was measured as their perception of how they adopt a particular innovation relative to this subgroup. Relative social media innovativeness (SMIrel) showed similar numbers as absolute innovativeness. The range was from 6 to 42, with a mean score of 24. In a test of the validity of this construct, simple linear regression was used to determine whether the SMIrel was related to the self-reported adoption of Snapchat relative to their social group. In support of this construct, those who indicated earlier social media adoption also scored higher on SMIrel ( = -0.438, t = -4.439, p < .001). Additionally, the means of the relative scores should be roughly equivalent. Using the grouped ages, an ANOVA was run, which showed significant differences amongst the groups (F(5, 223) = 3.148, p

< 0.05). The means were all roughly the same (~23) except for the youngest (25 and under, M = 27.0, SD = 6.32) and oldest (65 and older, M = 21.4, SD = 8.78) groups.

RQ3 asked if individuals could have two innovativeness scores, one relative and one absolute, that differ from each other. The data indicates this to not be the case.

Discussion

Study 1 was meant to lay a foundation for additional research in this dissertation.

The main concerns were the existence of multiple diffusion curves, two levels of innovativeness – one relative to society and one to the respondent’s more local social group. Also, this study was concerned with the adaptability and reliability of Hofacker and Goldsmith’s (1991) scale as it is important for the future studies of this dissertation.

Key limitations to the study of these phenomena became apparent during the analysis of the data. One issue surrounds the fluid and evolving nature of innovativeness within an individual. If innovativeness changes with age as noted, then current scores would be irrelevant in examining past adoption habits. This highlights a possible future examination of the development of innovativeness. No known study follows subjects and continually checks their innovativeness scores over time. While such a study would prove challenging as the instrument itself might affect future data collection, the within-subjects design could provide insights on what does affect the development and degradation of innovativeness.

To truly understand how a product or service diffuses throughout a population, adoption would probably be best measured in real-time. The nature of this dissertation does not permit this type of analysis, so it will have to be left for future endeavors. While

H2 is supported, the evidence is still weak and further study is warranted.

In short, support for H1 does not exist while the support for H2 is weak. While

H3: age correlated with innovativeness is supported with only 6 percent of the variance explained, research should look to uncover other more important predictors of this measure. It should also be noted that other predictors of innovativeness in the literature, such as SES, did not show as significant for this model. One interesting notion might be

the idea of social contact. Speculating that the average size of a younger person’s social structure may be larger due to larger groups of school peers and noting that education did not show differences in the mean, a t-test was run on those who were enrolled in college versus those who were not enrolled. This did show significant differences (t = -3.198, p < 0.005).

With study 1 completed, study 2 was set up to address a second concern before study 3. The next chapter will discuss the exploration of the motivations to adopt social media.

Figure 3-1. Facebook’s Google Trend data (black) over a standard product lifecycle (green).

Table 3-1. Reliability of Scales Scale Cronbach's Alpha

Group Technology Innovativeness 0.865

Social Media Innovativeness of Group (SMIgroup) 0.801

Relative Social Media Innovativeness (SMIrel) 0.846

Absolute Social Media Innovativeness (SMIabs) 0.848

Table 3-2. Snapchat adoption by age Age N Mean Adopt Year % adopters

Under 25 66 Late 2014 74.2% (49)

25 – 34 33 Early 2015 30.3% (10)

35 – 44 30 Mid 2015 43.3% (12)

45 – 54 38 Early 2016 15.8% (6)

55 – 64 38 2015 14.3% (5)

65+ 34 2015 2.9% (1)

Table 3-3. Adopter Categories Adopter Rogers (2003) Data

Innovators 2.5% 0.8%

Early Adopters 13.5% 11.8%

Early Majority 34.0% 40.0%

Late Majority 34.0% 30.0%

Laggards 16.0% 16.9%

CHAPTER 4 STUDY 2: A QUALITATIVE EXPLORATION OF SOCIAL MEDIA ADOPTION

Method

In concert with the primary goal of this dissertation, Study 2 explored the gratifications sought and the beliefs about the consequences of adoption of social media services. As this is more exploratory, a qualitative ladder interview process was used. The primary objective of these interviews was to address the overarching research question regarding motivations of social media adoption with rich qualitative data that could be used to create the final survey instrument for the third study.

Fishbein (1975) established that the first step for discovery of attributes is interviewing. As this research wants to determine core motivations, a ladder interview approach seemed to be the most appropriate. Reynolds and Gutman (1988) defined

Laddering as: “an in-depth one-on-one interviewing technique used to develop an understanding of how customers translate the attributes of products into meaningful associations with respect to self” (p788). They posit that ladder interviews are an excellent method for micro-level discovery of product elements. The authors explain that the process involves asking a series of direct questions, focusing on why an item might be important. Reynolds and Gutman (1988) outlined the procedures, cautions, and methods used in an in-depth approach to discover what features and products do for a subject, and how to map those to values. Additionally, the author of this dissertation used an adaptive technique in this probing to ensure complete understanding of terms.

The primary prompt of “What does that mean to you” aided the researcher in understanding the true meaning of words. For example, “success” to one subject may mean significant earning potential while for another it can mean achieving a degree of

career mastery. This technique of ensuring that the subject’s words’ meaning was extracted was developed through the researcher’s experience teaching adaptive sales techniques. The author of this study asserts that this adaptive laddering is an excellent way to discover the core needs and gratifications both sought and received by the adoption and use of media.

Results

The overarching theme of this dissertation asks why individuals adopt social media (RQ1) and how do these motivations to adopt social media change over time

(RQ2). This exploratory study has provided valuable insights into those questions and will help design a reliable method of determining the answers. First, this section will describe the sample, then it will explain the themes surrounding the gratifications sought. Finally, before entering a discussion, it will elaborate on the dominant reasons elicited for non-adoption, failed adoptions and discontinuance.

Description of the Sample

Two groups of individual subjects were interviewed during the summer of 2017.

The first group consisted of 21 undergraduate students at the University of Florida enrolled in an introductory marketing course. This sample skewed slightly female at 57 percent versus 43 percent male. The average age was about 21. As the student sample was recruited through an extra credit process, subjects were required to participate in

45 minutes of research. To meet this requirement, the survey from Study 1 was used to also assess innovativeness. The average absolute innovativeness score was approximately 24, similar to the national average from Study 1. To ensure that all motivations were explored, eight non-students (the second group of subjects) were recruited from the Gainesville community in an intercept manner at a local brewery and

a dog park. This non-student population was more diverse in age, and ranged in age from 23 to 71.

While there were more non-adopters in the non-student population, themes noted from the student population were repeated for both adoption and non-adoption in the group, even if the context was different. For example, while some students reported joining Facebook to join groups to network in the college environment, adults reported the same theme, but regarding their professional affiliations. While the total population was 29, themes started to repeat and approach saturation around the middle of the study. As such, the researcher is confident that the most common of themes were uncovered. The researcher also recognizes that outlier themes may appear as a result, and Study 3 will need to address that possibility.

Social Media Use

To check unaided recall subjects were first asked which social media services they belonged to or were part of. If one of the more popular social media services was not mentioned in this set, subjects were then prompted with the missing social media services to check aided recall. Table 4-1 details the data comparing aided vs. unaided recall. It is worth noting that though Snapchat’s unaided recall was low, many subjects did not view or think of it as social media per se. Of the sample, there were only three total non-adopters.

Gratifications Sought

Data was encoded in a map-type format to allow for the discovery of themes from the responses given to the interviewer. The laddering approach revealed more than just the gratifications sought by adoption, but also the underlying needs in many of the interviews. This would be expected as Katz et al. (1974) explain that gratifications

sought often lead to underlying needs. At times the path from gratification to need may cross. For example, a subject that indicates that they joined Facebook to join groups might be looking to find or join groups to build social capital. That social capital was used to allow for networking that could help them jump start their career, and a career would be important in order to have the basic necessities in life (need: physiological).

This might be compared to joining LinkedIn for career advancement, but crossing over, it might be to have a higher income such that they can afford to travel and experience life (need: self-actualization). This research focused on the more easily articulated and clarified gratifications sought. Using an adaptive communication technique (Khalsa and

Mahan, 2008) of probing, generic responses such as “to keep in touch” were expanded upon. “Keeping in touch” for some meant active communication via messaging within the service, while for others it meant a more passive surveillance and/or posting activity.

Over 100 motivations (or gratifications sought) were parsed from the responses.

These were categorized into 8 different themes starting with the original four commonly discussed themes in the original literature (Katz et al. 1974): Interpersonal

Relationships, Escape/Diversion, Surveillance, and Personal identity. Three other themes emerged as well. These themes may have been new and not relevant for older traditional media: Curiosity/Novelty, Social Capital, and Achievement. This study, while understanding the existing categories discussed in the original research, operated under the philosophy that new or different categories may exist. As such, new contexts and gratifications that would be under the umbrella of the original themes suggest a re- operationalization of the original paradigm to make room for newer interactive media.

These themes will be explored next and are presented in Table 4-2.

Theme 1: interpersonal relationships.

McQuail, Blumler, and Brown (1972) discussed personal relationships simply as referring to a person’s relationships with other people. Katz et al. (1974) expanded that to include social utility as well. These authors noted the complexity of this topic. Indeed, in modern times this complexity expands further as communicating over the media of the 1970s was not something the authors would have considered. As such, it is included here as related to interpersonal relations, as it was often used in maintaining those relationships. Another concept, social capital, is also important, but will be discussed later.

Laddering helped uncover how this theme applies to social media. Without question this was the most dominant and complicated theme with 50 statements related to interpersonal relationships. Even within that, there were several sub-themes that overlapped. Sub-themes included active communication, FOMO (or “fear of missing out”), being involved with friends, sharing their lives with friends, supplementing in- person interaction. Some subjects discussed how it was important to be part of a service in order to have something to talk about in person after seeing a post on social media. Facebook in particular dominated in this theme. The concept of “fear of missing out” (FOMO) also appeared here, with some subjects indicating that they needed to be aware of what was going on in the lives of those in their social circles. It would be reasonable to argue that this could be “social surveillance” and belongs with that theme, however, when it comes down to the original operationalizations of the themes, surveillance is more about general worldly knowledge or information while any type of social surveillance is more related to the original idea in this interpersonal relationship theme, that is, the desire to have something to connect and bond over. The motivation

to know what others are doing for the sake of connecting appeared frequently in

Facebook, but also in Snapchat and even Instagram with some degree of regularity.

Communication. “To communicate” was a frequent reason for the adoption of many of the social media. “Communication” means different things to different people.

As with above, many subjects would passively communicate by posting or creating content relevant to their lives to update their friends or followers. Active communication was also noted on those social media that allowed it. Of the four social media services that will be examined more in depth in study 3, all offer some form of private messaging.

The desire to actively communicate through messages also appeared. Active communication facilitates interpersonal relationships and as such, this dissertation asserts that it belongs in this theme. However, further research and analysis in study 3 may dictate that it be assigned its own theme.

It should not come as a surprise that media that is social in nature would show significant use to facilitate interpersonal connections. There are volumes of trade articles and pundit discussions that reveal how social media has changed the nature of our personal interactions and our communication in general. This is to the delight of some, and the derision of others, even within the scope of the interviews of this study.

Theme 2: escape/diversion

Escape and diversion can refer to passing time with media, or using it as stress relief, to escape life, or emotional release. For adoption, this theme emerged primarily with Instagram adoption. It might be worth noting that while many might use Facebook to pass time, that was never articulated as a reason for adoption. This theme exists in this research much as it has in the original research.

Theme 3: personal identity

The consumption of media to manage or explore one’s identity is not new, but in a time when an individual’s social media presence communicates something about the person and thus is the message itself, the crafting of a personal identity is highlighted even more. Despite this, only seven statements regarding identity management were identified with regard to adoption itself. In one notable case, a subject deleted their

Myspace account and immediately started a Facebook account to recreate an “older” identity upon moving to college. Identity management was a theme throughout this subject’s interview and could be seen in their online and offline activities.

The curation of a public identity is discussed within social presence theory

(Biocca, Harms, & Burgoon, 2003). With the opportunity to showcase one’s identity online, individuals have an opportunity to attempt to craft a public perception. The implications of this are discussed in the trade and academic literature and extend beyond the scope and intent of this dissertation.

Theme 4: surveillance

Surveillance is related to the search and consumption of information to make more informed decisions or know more about the world. Like escape, this theme remains largely unchanged from the original. As a primary motivator, this showed only in Twitter adoptions, however, in subsequent discussions on use, the theme emerged in both Facebook and Instagram as well. For those subjects who were far from home in particular, social media offered an opportunity to follow events from their home in a passive way that would have been unheard of before social media’s propagation. This adds a certain value that traditional news sites would not.

The significance of using social media for news cannot be overlooked. The controversies surrounding the 2016 U.S. presidential election and the pervasive propaganda surrounding it underscore not only how social media is now a popular way for individuals to stay informed, but also the dangers of the “echo chamber” and cognitive dissonance in repeating and consuming only the news that reinforces one’s beliefs – something that social media facilitates.

Theme 5: curiosity/novelty

This new theme refers to adoption motivations where the user wants to explore the medium. While FOMO (fear of missing out) appeared in some responses to refer to a fear of missing out on friends’ stories, it also appeared here under the idea of exploring what the “buzz” might be about. As one might imagine, this appeared when a social media was relatively new to the subject’s group, but not necessarily brand new, as it was almost always associated with other friends’ use. Novelty was another related concept here, and often relates to the idea of something that was different. Novelty appeared early in Snapchat and Instagram’s lifecycles while curiosity appeared when a social media was more established but a person was more interested in what others were talking about. Both dimensions appeared when SMIabs scores were higher and might suggest a relationship.

This new concept helps explain how innovations might be adopted before critical mass is achieved as well as how the concept of “buzz” works. Curiosity and Novelty are two small and very similar dimensions. Curiosity is generated by word-of-mouth buzz which works to create interest to explore media that others have adopted and are talking about. Novelty was identified in statements that suggested that new services might appeal to those who are just curious about new media per se.

Theme 6: social capital

This new theme emerged from many of the later adopters of Facebook, and all adopters of LinkedIn. Of the responses, 10 stated a more goal-oriented approach to social media adoption whereupon an individual would adopt social media to gain friends or relationships. This is different from the concept/theme of Interpersonal Relationships where an individual would be maintaining those relationships. Building social capital bifurcates into two areas: social capital for professional gain and social capital for friendships. With Facebook, building social capital for professional gain was more common than building general friendships. If this bears out with a larger population, it might suggest that individuals are more interested in using social media to maintain friendships than to start new ones, except when focused on achievement, the next theme. This is in line with Ellison, Steinfield, and Lampe’s (2010) finding that connecting with strangers is not normal behavior and most likely the individuals are looking to connect with the social group online, and the individuals offline.

Theme 7: achievement

A new, very goal oriented theme, achievement primarily appeared with LinkedIn adoption. Here, individuals can be seen adopting social media specifically to increase their chances of getting a job, career, and making money. With LinkedIn, this could be expected as it is specifically designed for this purpose. When using the ladder technique, subjects who joined Facebook to join groups related to their professions or majors and build social capital had achievement as an end goal. Interestingly, career leading to success means different things to different people and forks into three prongs: career to be secure and stable, career for financial gain for luxury, and career to achieve a sense of mastery or respect. Success had many definitions to the student

sample. This dimension is more difficult to distinguish or isolate compared to the other articulated motivations. Especially with Facebook, this dimension might be interrelated to the desire to build social capital.

Themes of non-adoption

RQ6 asked: Why do some individuals choose not to adopt social media? This research provided insights into non-adoption in line with the literature. Non-adoption, failed adoption, and discontinuance (through harvesting or deletion) were also explored in the interviews, when possible. Diffusion of Innovations (Rogers, 2003) provides the theoretical reasoning for situations of non-adoption. A few subjects indicated no longer using Myspace due to the technological inferiority of the product, and moving to

Facebook as a result (Rogers called this replacement disenchantment). Some failed adoptions are due to disenchantment discontinuance, as in the case of a college male who found Twitter “boring.” Related to the theory of critical mass, Twitter, in particular, would get abandoned when friends of the subject had abandoned the service. For the bulk of the non-adoptions, two themes dominated: a lack of compatibility and a lack of time. For many, the social media was “stupid” or was “a bad way to communicate.”

Responses along these themes indicated a lack of compatibility with values. One subject in particular stated that he is more traditional in his approach to life and social media does not mesh well with his approach. Especially amongst the more goal- oriented individuals, time was frequently cited as a reason to not adopt social media. To paraphrase one subject, “I’m a business student, I just don’t have time for stuff like

Twitter.” Finally, a few subjects did indicate a lack of significant knowledge of a social media service as a reason for non-adoption. A final theme in discontinuance emerged from compulsory adoption. Those who were forced to adopt some media (usually

Twitter or LinkedIn) for a class and subsequently saw no value once the authority figure’s power was removed would then discontinue use of the service. When discontinuance was observed, subjects who deleted their profile also indicated a concern for identity management.

Discussion

This study was designed to explore the motivations for adoption (and discontinuance) of social media. While the low sample size and recruitment methods warrant caution about drawing generalizable conclusions, the data reveal promising findings regarding this phenomenon. In particular, the data indicate changing adoption habits relative to the stage of the product lifecycle and innovativeness scores of the adoptee. With Facebook, a theme emerged with social capital as the dominant adoption reason in the introductory and current stage (be it late maturity or decline), with relationships as the focus during the maturity stage. It is worth noting that the newsfeed feature that facilitates the social connection many subjects cited did not exist immediately. In general, the qualitative laddering exploration of the motivations to join has added a richness that was necessary. Quan-Haase and Young (2010) suggested that “everyone I know is on Facebook” was related to fashion or a desire to show a connection to a trend, where the laddering interviews suggested that it was more of a desire to not be left out of people’s lives or upcoming events.

Katz et al’s (1974) discussion of uses and gratifications discussed some limitations in the original research, namely the needs or origins (social or psychological) of the gratifications sought. This research does not find utility in exploring the uses and gratifications of social media that deeply. Related to this is a criticism of articulating why a user joined a particular service. While some subjects had trouble recollecting, the vast

majority were able to recall enough to articulate what motivated them. Those dimensions enough should aid product designers and advertisers in understanding and creating a user profile. Moreover, users could recollect not only the surface level reason

(i.e. “all of my friends were doing it”) but when guided could remember why that was important in the past (“at the time I was curious what they were doing” versus “at the time it was important to belong”). What is important to note, based on how gratifications cannot be uniformly mapped to needs without context, is that instruments such as surveys may not be enough to understand the complex mental directions that link gratifications to needs. For advertisers looking to understand adopters at any particular stage, it is important to avoid assumptions as to what needs a user is looking to address. As observed here, the user may not fully understand or recall the underlying gratification being sought.

It is also worth noting that the uses and gratifications assumption – that researchers make no judgements of the value of the media – is important. Many social media non-users and users were quick to judge social media. What this study has shown is that our society evolves to use all media as a tool in certain goal-driven contexts. The harshest critics interviewed primarily articulated that social media was a waste of time. Indeed, we can see that just as one person’s trash is another person’s treasure, what is perceived by some as a waste of time is another person’s path to success or enlightenment.

The data also show some clear sought-after gratifications beyond the original uses and gratifications paradigm that are worth exploring in the next study. The final

step of this project was to synthesize the findings from the first two studies and examine the remainder of the hypotheses and questions in study 3.

Table 4-1. Social Media use in the Sample Total Users Unaided Recall Aided Recall

Facebook 26 26 0

Instagram 21 20 1

Snapchat 22 13 9

Twitter 12 11 1

Table 4-2. Statements identified in interviews, categorized into themes related to UGA # Statement Theme 1 I wanted to show what’s going on in my life Personal Relationships 2 I wanted to share moments with people Personal Relationships 3 I wanted to keep in touch with people Personal Relationships 4 I wanted to build friendships Personal Relationships 5 I wanted to be part of the in-crowd Personal Relationships 6 I wanted to see how people change Personal Relationships 7 I wanted to know when things were happening Personal Relationships 8 I wanted to be aware of events Personal Relationships 9 I didn’t want to be left out Personal Relationships 10 I wanted to see what friends were posting Personal Relationships 11 I wanted to see what other people were seeing Personal Relationships 12 I didn’t want to be the odd-one-out Personal Relationships 13 I didn’t want to miss out on what other people were doing Personal Relationships 14 I wanted to get to know people Personal Relationships 15 I wanted to see what other people were up to Personal Relationships 16 I wanted to keep up with friends Personal Relationships 17 I wanted to interact with friends Personal Relationships 18 I wanted to be involved with friends Personal Relationships 19 I wanted to be in-tune with what was going on socially Personal Relationships 20 I wanted to be more social Personal Relationships 21 I wanted to connect with friends that were on it Personal Relationships

Table 4-2. Continued. # Statement Theme 22 It was a way to be with friends Personal Relationships 23 I wanted to have something to talk about Personal Relationships 24 It was a better way to communicate Communication 25 It was a more personal way to communicate Communication 26 It was easier to talk to distant friends/family Communication 27 I wanted to communicate with friends/family Communication 28 It was a more private way to communicate’ Communication 29 It was an easier way to chat or communicate Communication 30 I wanted to find new friends Social Capital 31 I wanted to find/join clubs or groups that appealed to me Social Capital 32 I wanted to network Social Capital 33 I wanted to find and/or connect to classmates/co-workers Social Capital 34 I wanted to find/connect old friends Social Capital 35 I wanted to find and/or connect to people in my field Social Capital 36 I wanted to post pictures Escape/Diversion 37 I liked taking pictures Escape/Diversion 38 I wanted to use it to discover new things Escape/Diversion 39 I wanted to see art/pictures/photos Escape/Diversion 40 I wanted to see things that interested me Escape/Diversion 41 It looked like fun Escape/Diversion 42 Nothing better to do Escape/Diversion 43 I wanted to pass the time Escape/Diversion 44 I was bored Escape/Diversion 45 I wanted to follow famous people Escape/Diversion 46 I wanted to see other places Escape/Diversion 47 I wanted to use it to get away from reality or day-to-day life Escape/Diversion 48 I wanted to use it to play games Escape/Diversion 49 I wanted to use it to access games or other sites Escape/Diversion 50 I wanted to stay up-to-date on information Surveillance 51 I wanted to keep up with what’s popular and/or follow trends Surveillance 52 I wanted to know what was going on in the world Surveillance It’s a good way to get news/information, I wanted to stay 53 informed Surveillance 54 I wanted to create my identity Identity 55 I wanted to show who I am Identity 56 I wanted to display group/club/cultural membership Identity 57 I wanted to show a different version of myself Identity 58 I wanted to re-invent myself Identity

Table 4-2. Continued. # Statement Theme 59 I wanted to discover who I am Identity 60 I wanted to try it out Curiosity/Novelty 61 It seemed new/different/interesting Curiosity/Novelty 62 I wanted to see what people were talking about Curiosity/Novelty 63 I didn’t want to miss out on anything Curiosity/Novelty 64 I was curious Curiosity/Novelty 65 To help find a job Achievement 66 To help start my career Achievement 67 to get/display connections Achievement 68 to boost my resume Achievement 69 to connect with recruiters Achievement 70 to connect with professionals Achievement to help me professionally 71

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CHAPTER 5 STUDY 3: A QUANTITATIVE EXPLORATION OF SOCIAL MEDIA ADOPTION MOTIVATIONS ACROSS THE PRODUCT LIFECYCLE

Introduction

Study 3’s purpose was to combine the results of the previous two studies and finally explore the theme and questions of this dissertation in their totality: understanding why individuals adopt social media and how those motivations change over time. Study 1 established the innovativeness measure as a reliable way to measure absolute social media innovativeness, as well as a way to measure the life cycle of at least one of the services, Facebook. Study 2 helped to explore the motivations that a user might articulate, while also understanding the inherent needs that created them. After categorizing these motivations, potential survey items were identified. Using the data from the first two studies, an instrument was created to assess the remaining questions and assertions of this dissertation. This final method section will discuss the instrument used and explain the sample. More importantly, it will finally evaluate the remaining hypotheses one at a time as part of the results. There will be some discussion on issues of the results and analysis, however, larger themes will be addressed in the next chapter.

Method

Survey Instrument

Using the results from the first studies, a survey instrument was constructed (see

Appendix B). This instrument was designed to assess absolute social media innovativeness along with the adoption times of Facebook, Twitter, Instagram, and

Snapchat. Subjects were also polled on their attitude towards adoption of each medium at the time they adopted to be able to check reported attitude with calculated attitude.

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An open-ended question asked each user to explain why they adopted the service in order to allow a free response unaffected by the motivations they were about to be presented. For non-adoption, users were asked to explain why they have not adopted.

For each service adopted, users were then presented with a grid of themes as motivations identified in study 2. This grid allowed the subject to select which motivations applied, how well they thought each medium would support each motivation, and the importance of each motivation. Fishbein and Ajzen (1980) suggested that this method was an acceptable way to assess and calculate the cognitive algebra, although they identified the bipolar measures discussed in chapter 2 as preferred. Following the selection of motivations, subjects were asked about social pressures to adopt, and the motivation to comply with those social pressures from friends, family, coworkers, and authority figures.

Reliability of the instrument

For each social media service, reliability for the four questions was calculated. As presented in Table 5-1, reliability was acceptable. Reliability for the importance of each motivation to adopt, as well as the belief that each service would satisfy the motivation, both exceeded a Cronbach’s alpha value of 0.97. For social pressure to adopt, both the strength of the pressure and the motivation to comply had Cronbach’s alpha values between 0.82 and 0.89.

Results

Description of the Sample

Before describing the sample, it is important to note an issue during the data collection process. A project manager at the panel service firm, InnovateMR, added an extra zero to a variable in their algorithm which generates the sample. This extra zero

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essentially produced a sample of all male subjects. This male sample was surveyed from the morning of Saturday, September 23 to the night of Sunday, September 24

(2017). Once the error was caught, Innovate generated an equal-sized female sample over the course of Monday evening, September 25, into Tuesday early morning. Any gender differences can be confounded with the type of individuals willing to take a survey over a weekend as opposed to a weekday evening.

The survey was sent to a total of 6,189 individuals. In total, a panel of 1,627 subjects completed the survey in the same manner as the first study, but for each gender separately. After using the data cleaning measures employed in Study 1,

N=1,026 responses were retained for analysis. About half of the subject mortality could be ascribed to the overwhelming nature of the motivation grid. The time to complete depended on the subject’s social media use, but in general most survey responses were completed in 20 to 25 minutes and it was not common for the survey takers to exceed 25 minutes. The sample was evenly split between males and females by the nature of the sampling process mentioned above. The median age of the sample was

45 years old with ages 18 through 86 represented. Individuals of every age in that range are represented in the sample. The sample showed even diversity across educational status, with 20% having high school education, about 78% having some form of a college, professional, or post graduate degree and only two percent having less than high school education. The income level of the sample was also diversely spread amongst brackets under $100,000 annually, though some upper income brackets were represented. Finally, 41% of the sample was unemployed while the remaining 59% were employed either full or part-time. While this demographic data may not be fully

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representative of the US population, the goal of achieving generational diversity was met in order to explore more means than might have been achieved through a sample of college students.

The median absolute Social Media Innovativeness (SMIabs) score of the sample was 20, ranging from all possible values of 6 to 42. This was smaller in this sample compared to study 1’s median of 24.

In terms of social media use, Facebook dominated with 71 percent of the sample having reported adoption of the medium. Twitter was used second to Facebook with about 39 percent penetration in the sample. About 34 percent reported Instagram adoption. Snapchat, being the new kid on the scene, had 24 percent adoption.

Analysis

Factor analysis

A factor analysis (table 5-4) was conducted to determine the empirical categories of the motivations identified. For Facebook, two factors were identified. The first factor carries the most elements, but the top items by variance explained focus on social connection and interpersonal relationships, with FOMO dominating. The second theme focused on the communication items. The top five items sorted by variance explained were retained for a reliability analysis. Reliability analysis was conducted and was found to be 0.844 for factor 1 and 0.890 for factor 2.

Variable calculations

Due to the varying number of statements for each theme, it was necessary to create a method to be able to compare the scores for adoption motivation. To compare the adoption rate for each theme, a variable was created for adoption within that theme.

If any theme was identified as being selected by the respondent (clicked), then that

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theme would receive a ‘1’ as identified versus a ‘0’ for not being identified. Then, a mean of this score was calculated across the study population to allow for similar comparisons. Thus, this score could range from 0 to 1.

Attitude and subjective norm were computed as per Equations 2-1 and 2-2 respectively with each identified theme. All programming and calculations were conducted in SPSS version 19.

The causes of adoption

RQ1: Why do individuals adopt social media? The overarching theme of this dissertation asks why people adopt social media; the data (presented in Table 5-3 along with social pressure) provides insight into that. For all media except for Snapchat, interpersonal relationships dominated. For Snapchat, curiosity was reported just slightly more than interpersonal relationships, but close enough for them to be even. This is not too surprising as it is still a newer and different form of social media and as mentioned previously, obfuscates features. For Facebook, users also indicated communication, social capital, and escape as reasons for adoption. Twitter users identified curiosity, escape, and surveillance as reasons to adopt the microblog service. Instagram users identified escape/diversion nearly as much as interpersonal relationship as a reason to adopt that medium in addition to curiosity. Snapchat users identified communication as a reason along with escape and diversion. Many of these insights are in line with the results of study 2 as the primary motivator. As the nature of the survey could not force subjects to identify just one motivation, many identified multiple, creating the high percentages in Table 5-3.

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Social Pressure1. Subjects could identify the outright pressures that they felt to join from family, friends, coworkers or classmates, a parental authority, or another type of authority. The nature of the question addressed explicit pressure directly from a person suggesting or telling a person to join more so than implicit pressure to conform, though it is possible that implicit pressure might have factored into a subject’s response.

Similar to the motivations, each subject was identified if they’d indicated at least one of these pressure types. These values are also reported in Table 5-3. It is worth noting that in all cases social pressure was indicated by more than 60 percent of the sample.

Snapchat amounted to the greatest social pressure at 80 percent with Facebook trailing at almost 75 percent. Instagram was next with just over 68 percent of the sample reporting some form of social pressure. Twitter had the least with 60 percent reporting some form of pressure. It is worth noting that data from study 2 and the open-ended questions in this survey instrument suggested that social pressure and curiosity may have factored into a decision to adopt, but some users discontinued adoption if the service was complicated, of no interest, or if friends also stopped using it. This was especially true of Twitter.

Tests of hypotheses

H3: Innovativeness and age. In a repeat of the test of absolute social media innovativeness being correlated with age, regression analysis was conducted. The relationship was again shown to be significant (= –0.369, t = – 12.689, p < .001). Here

1 Social pressure was calculated using Equation 2-2 as outlined by Fishbein and Ajzen (1975). For those indicating pressure, the level of the pressure was multiplied by the motivation to acquiesce to the pressure. For total social pressure each element was summed in accordance to the Theory of Reasoned Action to create the overall normative term.

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13 percent of the variance was explained (R2 = 0.136, F(1,227) = 161.008, p < .001). It is worth noting that this is double the variance explained in the first study, but is still a small number. Still, this underscores the support for this relationship.

RQ4b. RQ4b asked how the motivations to adopt contributed towards adoption while H4 predicted that the attitude formed from these motivations would have less influence later in the product lifecycle. Facebook, having the longest adoption cycle, was used to test for this phenomenon. As predicted, an ANOVA revealed significant differences in attitude amongst the years (F(13, 772) = 3.838, p < 0.001) with the means plot suggesting a decreasing trend. Just as attitude decreases, so too does the number of incidences of individuals reporting motivations to adopt. In all cases but “identity,” the mean number of reported motivations over the PLC decreases.

H4. Hypothesis 4 predicted that the social normative term will assert more influence later in the life cycle. First, a one-way ANOVA was used to test for changes in the social pressures to adopt over time. Contrary to expected results, there was no significant change in the social pressure to adopt over the product lifecycle (F(3,

790)=0.347, p=0.791). As mentioned above, the social pressure is made up of multiple social pressures. The bank of questions for each split up into friends, family, classmates or coworkers, parents as authority figures, and other authority figures (such as a work superior). To check for differences in the types of social pressure, another ANOVA was used. Over the course of the lifecycle of Facebook, two were significantly different in different stages of the PLC: Friends (F(3,754=6.089, p<0.001) and Family

(F(3,749)=2.703, p<0.05). A Bonferroni post-hoc test showed that, for “pressure from friends,” the difference was between introduction and maturity (p<0.001) while “pressure

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from family” significantly differed between introduction and decline (p < 0.05). The post- hoc results are presented in Figure 5-9.

H4 expected that pressure would rise while attitude would drop. While the prediction for attitude was validated, the prediction for social pressure was not. As such,

Hypothesis 4 is not supported.

H5: Primary Gratification Sought varies by Life Cycle Stage. Users identified gratifications sought (as motivations) from adoption and their year of adoption. These gratifications were categorized in study 2 into interpersonal relationships, communication, social capital, escape, surveillance, identity, and curiosity.

Communication and social capital were assigned their own category to allow for more resolution in discovering differences. ANOVAs were run on each category of gratification to check for differences. The ANOVAs revealed that the mean incidence of reporting for each motivation/gratification decreased throughout the product lifecycle except for personal identity. The results of the omnibus ANOVAs are reported in Table

5-2. A Bonferroni post hoc analysis was used to examine where the differences existed along the lifecycle. For interpersonal relationships, communication, social capital, escape, and curiosity, the decline stage was significantly different from the introduction stage. For surveillance, the Bonferroni test revealed that the introduction stage and the decline stages were significantly different. Table 5-2 and Figures 5-3 through 5-8 show these phenomena.

While significant differences were found for the changes in the motivations from one stage of the PLC to the next, the amount of each motivation had to be compared to the others. For each user, they were coded as 0 or 1 to indicate if they had articulated

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any one item in the category of motivations. Means were then calculated for each motivation to determine which was more represented. The means for each motivation were compared to each other within life cycle stage to determine which motivation dominated in any given life cycle stage. Pairwise t-tests were used to analyze differences of those means to see if any one motivation actually lead another, or if they were statistically tied. The results of these t-tests are reported in Tables 5-4 through 5-8.

Hypothesis 5 predicted that the primary gratification would vary by lifecycle.

There is some evidence to support this assertion, though is it not strong. For Facebook, during the introduction and growth stage, interpersonal relationships had the highest mean value. For the maturity stage, interpersonal relationships and communication were statistically tied. For the decline stage, personal relationships shared the top spot with communication, escape/diversion, and curiosity. This appears to be due more so to a drop of incidence of interpersonal relationships as opposed to an increase of the other variables. However, H5 is supported.

H6: Calculated Attitude vs. Reported Attitude towards Adoption: Hypothesis

6 predicted that the reported attitude towards adoption of a particular service would correlate with attitude calculated using the equations developed by the Theory of

Reasoned Action (Equation 2-1) (Fishbein and Ajzen, 1975). Supporting this assertion, a bivariate correlation showed a significant relationship (r = .448, n = 440, p < 0.001).

H7: Social Pressure and percentage of referents who’ve already adopted:

Hypothesis 7 was related to the concept of relative innovativeness. Study 1 sought to establish relative innovativeness as a construct and the element of how many of one’s referents had adopted was a part of that questioning. As there appeared to be some

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issues with this report, the question was also asked in Study 2. Since subjects all reported the same answer (“I adopted when all my friends did”), it appeared that this historical report was not an effective means of assessing this information. This hypothesis could not be properly tested and was not addressed in the survey, especially as subject fatigue was already a concern. Additionally, interview data from Study 2 would suggest that social pressure was not related to group size as those who indicated social pressure often claimed that the pressure was from a single individual and not necessarily the group.

Discussion

This third and final part of this dissertation was designed to assess the changes in the motivations to adopt social media, in this case Facebook, over time. Study 3 established that the motivations do change, though the evidence is not strong and more work should be done to establish or refute this finding. Interestingly, while individuals did highlight motivations that could be categorized with the uses and gratifications approach, the factor analysis did not highlight all of these categories. This brief discussion here will expand on the two major areas of study 3: the factor analysis and the causes of adoption.

Factor Analysis. The first point of discussion centers around the results of the factor analysis. It is worth nothing that only two themes dominated the analysis:

Interpersonal Relationships (of which FOMO dominated) and Communication. Study 2 suggested that communication may be its own theme, given the factor analysis results, this supports that operationalization. While the remaining analysis focused on the theoretical themes identified with the uses and gratifications approach, the dual themes should be addressed in future research. It may be that the source of the results of the

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factor analysis may be inherent in the general idea of Facebook’s prime purpose of presenting one’s self to people and interacting with people. What may also be of interest is the relation the other motivations may have relative to these two themes in the context of Facebook adoption, and how that might change in the study of other social media.

The Causes of Adoption. This study identified Interpersonal Relationships as the prime reason to adopt social media, particularly at the start of the lifecycle of

Facebook. As Facebook aged and penetrated society, this became less of a factor.

Some elements, such as communication, may have emerged later as Facebook did not create the messenger element of the service until later. This may complicate future research of social media services as they often add new features as time passes.

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Figure 5-1. Social Pressure (black) compared with reported attitude towards adoption (red) over the life cycle of Facebook.

Figure 5-2. Means plot of social pressure over PLC

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Figure 5-3. Social pressure’s components by lifecycle. Error Bars are 95% CI.

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Figure 5-4. Means plot of Curiosity over PLC

Figure 5-5. Means plot of escape/diversion over PLC

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Figure 5-6. Means plot of communication over PLC

Figure 5-7. Means plot of social capital over PLC

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Figure 5-8. Means plot of interpersonal relationships over PLC

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Figure 5-9. The Facebook product lifecycle as modeled by adoptions per year (blue), total adoptions (red), Google Trends Data (black) together with a stylized PLC (green)

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Table 5-1. Reliability of Motivation to Adopt and Social Pressure scales (Chronbach’s Alpha) Importance Belief SN: pressure SN: motivation Facebook 0.977 0.981 0.850 0.827 Twitter 0.985 0.983 0.867 0.890 Instagram 0.977 0.976 0.860 0.860 Snapchat 0.982 0.981 0.859 0.867

Table 5-2. ANOVAs for motivations for the adoption of Facebook throughout the PLC Interpersonal Relationships F(3, 790) = 6.973* Communication F(3, 790) = 6.292* Social Capital F(3, 790) = 12.446* Escape/Diversion F(3, 790) = 5.865** Surveillance F(3, 790) = 2.879** Identity F(3, 790) = 2.364, p=0.070 Curiosity F(3, 790) = 7.171* Social Pressure F(3, 790) = 0.347, p=0.791 Attitude F(3, 785) = 11.589*

*p < 0.001, ** p < 0.05

Table 5-3. Social Media Adoption Motivations and Pressure Facebook Twitter Instagram Snapchat Adopters 730 400 344 242 Interpersonal Relationships 91.9% 85.5% 86.6% 90.1% Communication 88.2% 60.0% 63.7% 78.4% Social Capital 81.0% 61.8% 64.0% 52.9% Escape/Diversion 87.2% 78.7% 84.9% 82.6% Surveillance 63.0% 68.9% 53.2% 46.3% Identity 38.4% 36.8% 48.0% 48.3% Curiosity 84.6% 78.7% 79.7% 92.1%

Social Pressure 74.7% 60.0% 68.3% 81.0%

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Table 5-4. Factor Analysis Theme 1 2 I wanted to see what other people were seeing 0.754 I wanted to have something to talk about 0.739 I didn’t want to miss out on what other people were doing 0.723 I wanted to get to know people 0.721 I wanted to know when things were happening 0.715 I wanted to be more social 0.713 I didn’t want to miss out on anything 0.706 I wanted to stay up-to-date on information 0.703 I wanted to see other places 0.697 I wanted to create my identity 0.697 -0.4 I didn’t want to be left out 0.696 I wanted to see what people were talking about 0.696 I wanted to show who I am 0.695 I wanted to be in-tune with what was going on socially 0.695 I didn’t want to be the odd-one-out 0.689 I wanted to build friendships 0.686 I wanted to keep up with what’s popular and/or follow trends 0.685 I wanted to be part of the in-crowd 0.684 -0.3 I wanted to display group/club/cultural membership 0.683 -0.415 I wanted to find new friends 0.679 I wanted to show what’s going on in my life 0.675 I wanted to find/join clubs or groups that appealed to me 0.669 I wanted to use it to get away from reality or day-to-day life 0.669 -0.325 I wanted to use it to discover new things 0.668 I wanted to be aware of events 0.667 I wanted to network 0.662 It seemed new/different/interesting 0.661 I wanted to discover who I am 0.657 -0.463 It looked like fun 0.654 I wanted to share moments with people 0.653 I liked taking pictures 0.653 It was a more personal way to communicate 0.649 I wanted to see things that interested me 0.647 I wanted to follow famous people 0.647 -0.467 I wanted to re-invent myself 0.642 -0.517 I wanted to post pictures 0.638 I wanted to know what was going on in the world 0.636

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Table 5-4. Continued. Theme 1 2 I was bored 0.626 I wanted to pass the time 0.626 I wanted to see art/pictures/photos 0.624 I wanted to see what other people were up to 0.622 0.336 It was a better way to communicate 0.619 It was a more private way to communicate 0.615 I wanted to show a different version of myself 0.614 -0.451 I wanted to be involved with friends 0.612 0.455 I wanted to find and/or connect to people in my field 0.608 It was a good way to get news and/or information, I wanted to stay informed 0.604 I wanted to see what friends were posting 0.601 0.42 It was a way to be with friends 0.596 0.346 I wanted to see how people change 0.593 Nothing better to do 0.579 I wanted to try it out 0.563 I wanted to interact with friends 0.561 0.549 I was curious 0.555 It was an easier way to chat or communicate 0.552 0.357 I wanted to find and/or connect to classmates/co-workers 0.508 0.401 I wanted to use it to access games or other sites 0.501 -0.345 I wanted to use it to play games 0.496 -0.337 I wanted to communicate with friends/family 0.382 0.705 I wanted to connect with friends that were on it 0.479 0.627 I wanted to keep in touch with people 0.513 0.604 I wanted to keep up with friends 0.534 0.591 It was easier to talk to distant friends/family 0.44 0.536 I wanted to find/connect old friends 0.454 0.512 Extraction Method: Principal Axis Factoring.

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Table 5-5. Differences in the mean values of motivation indexes in the Introduction Stage Personal Surveil- Personal Curios- Mean Communication Social Capital Escape / Diversion Relationships lance Identity ity Personal Relationships 0.9559 - Communication 0.9207 2.561^ - Social Capital 0.8767 4.412 * 2.149 ^ - Escape/Diversion 0.9207 2.155 ^ 0, p=1.000 1.901, p=0.059 - Surveillance 0.6828 9.215 * 8.018 * 5.895 * 8.202 * - Personal Identity 0.4493 15.233 * 13.951 * 12.542 * 14.196 * 6.683 * - Curiosity 0.9119 2.71 ** .446, p=656 1.561, p=0.131 0.426, p=0.671 7.643 * 13.260 * - * p < .001, ** p < .01, ^ p < .05 Rank Order: [Personal Relationships][Communication, Escape, Curiosity][Social Capital][Surveillance][Identity]

Table 5-6. Differences in the mean values of motivation indexes in the Growth Stage Personal Surveil- Personal Curios- Mean Communication Social Capital Escape / Diversion Relationships lance Identity ity Personal Relationships 0.9221 - Communication 0.8847 2.860 ** - Social Capital 0.8287 5.363 * 3.129 ** - Escape/Diversion 0.8723 2.956 ** 0.666, p=0.506 2.122 ^ - Surveillance 0.6199 11.601 * 10.108 * 8.052 * 9.628 * - Personal Identity 0.3769 19.343 * 17.526 * 15.477 * 17.095 * 7.988 * - Curiosity 0.838 4.712 * 2.420 ^ 0.493, p=0.623 1.923, p=0.055 8.195 * 15.426 * - * p < .001, ** p < .01, ^ p < .05 Rank Order: [Personal Relationships][Communication, Escape][curiosity, social capital][surveillance][Identity]

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Table 5-7. Differences in the mean values of motivation indexes in the Maturity Stage Personal Surveil- Personal Curio- Mean Communication Social Capital Escape / Diversion Relationships lance Identity sity Personal Relationships 0.92 - Communication 0.8847 1.742, p=.083 - Social Capital 0.7886 4.892 * 3.129 ** - Escape/Diversion 0.8629 2.262 ^ 0.852, p=0.395 2.448 ^ - Surveillance 0.6346 7.911* 6.696 * 3.704 * 6.093 * - Personal Identity 0.3429 15.411 * 13.755 * 10.866 * 13.141 * 7.043 * - Curiosity 0.84 3.055 ** 1.715, p=0.088 1.680, p=0.95 0.943, p=0.347 5.500 * 12.299 * - * p < .001, ** p < .01, ^ p < .05 Rank Order: [Personal Relationships, Communication] [Curiosity, Escape] [Social Capital] [Surveillance] [Identity]

Table 5-8. Differences in the mean values of motivation indexes in the Decline Stage Personal Escape / Personal Mean Communication Social Capital Surveillance Curiosity Relationships Diversion Identity

Personal Relationships 0.7887 - Communication 0.7324 1.270, p=.208 - Social Capital 0.5634 4.187 * 3.188 ** - Escape/Diversion 0.7324 1.070, p=0.288 0, p=1.000 2.666 ** - Surveillance 0.493 5.422 * 3.880 * 1.093, p=0.278 4.369 * - Personal Identity 0.3099 8.020 * 6.776 * 4.549 * 7.151 * 2.991 ** - Curiosity 0.6901 1.837, p=0.070 0.686, p=0.495 1.911, p=0.060 1.000, p=0.321 3.820 * 6.194 * - * p < .001, ** p < .01, ^ p < .05 Rank Order: Personal Relationships, Communication, Escape, Curiosity] [Social Capital, Surveillance, Identity]

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Table 5-9. Results of Bonferroni Post-Hoc Test of differences for ANOVAs in Table 5-2 Theme Stage Comparison Bonferroni Value Interpersonal Intro Growth 0.888 Relationships Maturity 1 Decline* 0 Growth Intro 0.888 Maturity 1 Decline* 0.001 Maturity Intro 1 Growth 1 Decline* 0.003 Decline Intro* 0 Growth* 0.001 Maturity* 0.003 Communication Intro Growth 1 Maturity 1 Decline* 0 Growth Intro 1 Maturity 1 Decline* 0.002 Maturity Intro 1 Growth 1 Decline* 0.004 Decline Intro* 0 Growth* 0.002 Maturity* 0.004 Escape Intro Growth 0.556 Maturity 0.501 Decline* 0 Growth Intro 0.556 Maturity 1 Decline* 0.008 Maturity Intro 0.501 Growth 1 Decline* 0.032 Decline Intro* 0 Growth* 0.008 Maturity* 0.032

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Table 5-9. Continued. Theme Stage Comparison Bonferroni Value Surveillance Intro Growth 0.795 Maturity 1 Decline* 0.023 Growth Intro 0.795 Maturity 1 Decline 0.268 Maturity Intro 1 Growth 1 Decline 0.224 Decline Intro* 0.023 Growth 0.268 Maturity 0.224 Social Capital Intro Growth 0.903 Maturity 0.138 Decline* 0 Growth Intro 0.903 Maturity 1 Decline* 0 Maturity Intro 0.138 Growth 1 Decline* 0 Decline Intro* 0 Growth* 0 Maturity* 0 Attitude Intro Growth 0.574 Maturity* 0.003 Decline* 0 Growth Intro 0.574 Maturity 0.158 Decline* 0 Maturity Intro* 0.003 Growth 0.158 Decline* 0.037 Decline Intro* 0 Growth* 0 Maturity* 0.037

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Table 5-9. Continued. Theme Stage Comparison Bonferroni Value Curiosity Intro Growth 0.103 Maturity 0.273 Decline* 0 Growth Intro 0.103 Maturity 1 Decline* 0.01 Maturity Intro 0.273 Growth 1 Decline* 0.017 Decline Intro* 0 Growth* 0.01 Maturity* 0.017

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CHAPTER 6 CONCLUSION

General Discussion

This research started out with two overarching questions: Why do individuals adopt a particular social media and how do these motivations to adopt change over time. This dissertation answered those questions using a uses and gratifications approach together with the product lifecycle and the cognitive algebra outlined in the

Theory of Reasoned Action. A summary of the hypotheses is addressed in Table 6-1.

Social Media Adoption

Individuals adopt social media for one or more of several reasons: To facilitate interpersonal relationships, to build social capital, to escape from life, to engage in surveillance of information, to create or explore their identity, to satiate curiosity or a desire for novelty, and for achievement. These reasons for adoption build on the original themes identified by classic uses and gratification literature by presenting new gratifications in-line with newer interactive media. This research also identified the primary reasons for non-adoption or discontinuance of social media services. Users who don’t adopt often don’t due to time, a lack of understanding of the service, or a lack of compatibility with their values. These themes of non-adoption are in concert with

Diffusion of Innovations (Rogers, 2003).

Over the lifespan of a service, the motivations, or at least the strength of those motivations, can change. This research established that by the time Facebook was steeped in the decline stage, interpersonal relationships was no longer the dominant reason for adoption. The reasons for this remain yet unknown and future research may seek to address the causes of this phenomenon. Interestingly, this research discovered

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that the level of social pressure does not change over time for adoption of a service. It was expected that social pressure would change in some stages as explained with

Diffusion of Innovations and critical mass (Rogers, 2003), particularly as the utility of the service increased as more people started using the service. This may be in part due to the complexity of and changing sources of social pressure for social media. Diffusion theory, when it was developed, did not explore innovations that were social by nature such as social media and this research may have uncovered a weakness of the theory, or at least an opportunity for theoretical expansion and development. Specifically, innovations that are social in nature—those that foster a social connection--may not exhibit a change of overall social pressure, however the type of social pressure may change. As this research was more concerned with the loci of social pressure (friends vs. co-workers/classmates vs. authority figures), future research might explore the types of pressures exerted.

It is notable that social media as a media type may be adopted differently than traditional media. In particular, using diffusion theory, the effect of social pressure as an element of this adoption would normally increase over time. This might also be true of communication inventions. In this study, this does not seem to be the case. This may be due to the inherent social nature of the media. Unlike telephones, as a form of communication social media also acts as a method of creating an identity. Unlike mass media, social media is personal and customizable. Indeed, it is thought to have facilitated the election of the past two United States presidents as they used Twitter to create their identity and push their messages.

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As there is no established literature on the adoption of social media, this contributes to our understanding of this relatively new type of media. Additionally, this adds to the idea of using uses and gratifications to study social media. Ruggerio (2000) called for expansion of the uses and gratifications model to handle Internet-based media. Gangadharbatla (2011) proposed seven categories of motivation (need to belong, communication, entertainment, information, commerce, and self-expression).

Several of these motivation themes can be mapped into the themes identified here.

However, commerce was not identified in study 2’s interviews or the open-ended questions from study 3.

Social Media Non-Adoption

This research identified several reasons individuals do not adopt, or discontinue use of social media services. Gangadharbatla (2011) proposed several reasons users may not adopt social media in the framework of a technology acceptance model (self- efficacy, interface design, internet expertise, and third-party applications). While a few older users indicated a lack of understanding for adoption of Snapchat, none of these were indicated frequently in this study. The conclusion of this dissertation is that

Diffusion of Innovations (Rogers, 2003) is a far better theory for understanding the non- adoption of social media. The themes of non-adoption identified by diffusion theory and noted in this research are a primarily a lack of compatibility with values, and a lack of time. Those whose reasons to not adopt would fall in the area of complexity were rare and could be described as outliers. As social media continues to penetrate our society, this is not as likely to be articulated.

Discontinuance of the use of a service was explored, and the reasons here also are explained by diffusion theory as Facebook’s relative advantage was notable as a

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reason to leave Myspace, as well as the sub-theme of critical mass: many users left

Myspace or Twitter because friends simply were not using the service any more.

Limitations and Future Research

This study establishes a foundation to understand the phenomenon of social media adoption, a research area that has yet to be studied. While this research has created insightful data, its main limitation is the survey instrument. It should be noted that the procedure used by Goldsmith and Hofacker (1991) and Churchhill (1979) should be employed to purify and simplify the scales. The best way to clean these scales would be with more current data, with individuals who have adopted social media more recently and can better identify the reason for the adoption. To do this effectively and over time, this researcher recommends a longitudinal approach. The scales presented may have contributed to the low variance explained in the calculated vs. reported comparison of attitude. Additionally, this research leans heavily on a self-report of past activities. This self-report created problems with many of the potential constructs this research discussed. This was also central to not reaching the secondary objective of creating a method of using the traits of current adopters. Once the scales are cleaned, a longitudinal approach would be the best way to apply this research to study these changes. This longitudinal approach should also be employed in the study of current adopters to assess the change of innovativeness as new social media services or other develop. Additionally, interviews identified that motivations for adoption changed once services were used and continued to change. A within-subjects approach would allow a true understanding of how motivations for adoption and use change over time. Clearly, this is not something that can be done in the scope of a dissertation, but over the course of a research line. Finally, researchers looking to study the motivations

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to adopt should also understand the value of the ladder technique in this discovery process. Users’ immediate recollection for motivations do not always tell the whole story. This also contributes to a weakness of the survey instrument in looking to gain valid results. This trade-off in validity is unavoidable.

The next steps in this line of research would be to begin purifying the scale based on the data within this dissertation. This should coincide with regular polling of social media users in real time to check for adoption and motivations. Given time, this data can create a deeper understanding of how social media products diffuse in our population. Future research should also move beyond the limitations of arbitrary categories. Measurement using continuous variables and regression analyses can help discover how dynamic some of these relationships may be, particularly, what non-linear relationships might exist with innovativeness.

The concept and construct of relative social media innovativeness should also be examined, in addition to the methods of measuring this trait. This dissertation used a self-report to attempt to assess innovativeness. Future research might examine multiple ways beyond self-reporting to gain insights on innovativeness, but especially innovativeness relative to one’s close social group.

Theoretical Implications

Uses and Gratifications and the Theory of Reasoned Action

Uses and gratifications posits media is consumed for reasoned that can be categorized into four distinct categories: interpersonal relationships, escape/diversion, personal identity, and surveillance (Blumler and Katz 1974; Katz, Blumler, and

Gurevitch 1973). This research contributes to uses and gratifications literature by enhancing its usefulness for online interactive media, particularly social media, by

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noting that interpersonal relationships can be subdivided with two new categories: communication and social capital, and by adding a new category of curiosity.

Additionally, using the Theory of Reasoned Action and its approach to calculating attitude and adoption is a valuable way to add a theoretical nature to this approach.

However, while TRA worked well for this dissertation, the element of time as a resource cannot be ignored.

As time was a recurring theme of non-adoption, the use of the Theory of

Reasoned Action may be limiting with studying social media adoption. Earlier, this research asserted that the Theory of Planned Behavior (Ajzen, 1985) would not be necessary as social media are free services, ignoring the idea that individuals are exchanging personal information to use the site. As a reminder, the Theory of Planned

Behavior extends the Theory of Reasoned Action such that it considers whether the subject has the resources and is willing to commit the resources towards adoption

(Ajzen, 1985). Given the results in this study, this flaw in reasoning needs to be re- examined. Time is also a resource and it is possible that time could be factored in as a variable for social media adoption.

A theory of time economics exists (Becker 1965). This theory outlines the use of time in an individual’s utility calculations. Leclerc, Schmitt, and Dubé (1995) outline this theory in understanding time as a resource more valuable to some than money. Indeed, if some people view time as a resource and do not wish to waste time, then it would be reasonable to explore how time factors into adoption of otherwise free services, especially as these resources clamor for users’ time, and the propagation of social media may increasingly start to compete for users’ valuable time.

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Diffusion of Innovations and the Product Life Cycle

Diffusion of Innovations (Rogers 2003) is a vast theory that helps us to understand why individuals may adopt new innovations as ideas, habits, or products (as the product life cycle) and how these items can propagate through a social system, facilitated by traits of observability, superiority, lack of complexity, and trialability. This research has shown that diffusion theory can help illustrate why individuals do not adopt or discontinue use of social media as shown by Myspace losing ground to Facebook due to the new media’s superiority, a lack of compatibility with values, or elements of critical mass. This research also contributed to the diffusion literature in understanding that users’ reasons for adopting can change over time. Adoption before critical mass has also been expanded. While Valente (1996) explored threshold models with regards to social media, curiosity is a new area that can be explored.

Interestingly, the consistent role of social pressure in adoption of social media is, of course, another area worth expanding. This research has identified that the number of people who’ve adopted this type of innovation does not necessarily contribute or otherwise relate to adoption, as was expected. A deeper understanding of the nuanced social pressures involved might help illuminate how social pressure affects adoption for this and other innovations.

Methodological Contributions

Adaptive Ladder Interviews

The use of the laddering technique was an effective way to assess deeper needs. Using the adaptive, probing approach was an effective way to understand the deeper needs. Researchers should understand that, just as different research can operationalize terms differently, people can as well. This carries significant implications

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with survey design and researchers should be absolutely sure that they clarify what they mean in those surveys. Additionally, as the definitions and contexts behind many of the concepts we researchers study can change, it may be important for researchers to re- examine how a person defines their world and articulates it, and how they interpret things in the context of their realities. The adaptive laddering technique is important to this, but so too is understanding the body language and non-verbal cues that subjects give. Mass market literature (i.e. Mahan and Khalsa 2009) understand that emotional intelligence and some degree of training are necessary to be able to interpret this body language and allow the subject to run the interview, to some degree.

Google Trends

The use of Google Trends to chart phenomena has been expanded here. As illustrated, Google can be used by both researchers and professionals to help understand human trends in product or idea adoption. The Google Trends platform is flexible. The interface can be used by a researcher to specify geographic or time requirements. This can be effective for charting trends on a micro level as well as for the

US or the world as a whole.

Caution should be exercised, however, in over-relying on the data from this service. It may be best to couple this data with other techniques or at least, use it as a first step in answering a research question. While trend data was helpful in illustrating product life cycle trends, not all product life cycles look the same and indeed, adoption, use, and search relevance are not necessarily the same thing.

Managerial Implications

As this is a mass communication dissertation that also touches areas in advertising and marketing, it is important to understand how it can affect those applied

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fields. Advertising spending is still increasing, but social media spending makes up the lion’s share of that increase. Marketwatch (2016) reported that in 2015 total ad spending increased by 7 percent with digital driving that growth with a 34 percent increase.

Supporting the digital growth is social media, with a 71 percent increase in ad spending over 2014. Major players Facebook, Snapchat and Instagram have been looking to keep users entertained and are looking to increase video content use and advertising revenue from that video (emarketer 2017). Emarketer noted Facebook’s slowdown in growth that some of the data in this dissertation also support, and if users are shifting platforms, this research and related research might be able to uncover why and how the platforms can adjust and the advertisers can properly target. If, as Kumar et al. (2016) explain, firms need to understand how to foster consumer engagement on these platforms as it aids in profitability, it may help to understand what is driving a user from platform to platform to foster the proper content generation. Indeed, marketing managers looking to use social media to promote brands should understand who is adopting when, and why, and what the motivations to adopt are and how these motivations change over time. Future researchers can pair these motivations with personality types to create better product offerings of interest to those types of consumers. This is also important for the service developers and providers so that they can provide relevant advertising less likely to annoy or turn off new users before critical mass has been achieved.

Product developers can also use this research to understand that motivations to adopt can change over time and they have to be aware of this as they add or remove features. It also underscores that existing social media services should be aware that

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users are not tied to the site, but the bundle of benefits and if a new service captures that motivation better, users may start to leave.

One key finding that this research found notable, if not obvious, is that social pressure drives social media adoption. While it seems obvious, the fact that it was identified as a motivation to adopt a particular media cannot be ignored. Thus, the power and influence of social pressure on the areas of adoption of other products and services, as well as consumer decision making, especially in a social, digital landscape, are worthy of continued exploration.

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Table 6-1. Hypotheses Hypothesis Assertion Support H1 The current stage of diffusion (or product life cycle) No will correlate with the innovativeness score of the modal adopter of the social system of measurement

H2 An innovation (or product) that has established itself Yes in multiple social groups will be in two or more stages of the PLC.

H3 Absolute Social Media Innovativeness will be Yes correlated with age such that younger individuals will exhibit more innovation.

H4 The social normative term will exert a greater No influence later in the product life cycle while attitude will show less influence

H5 The primary gratification sought by a social media Yes service will vary by life cycle stage.

H6 Calculated attitude towards social media adoption Yes will correlate with reported attitude of social media adoption. H7 The subjective normative component will be Not Tested positively correlated with the percentage of one’s referents who have also adopted a social media service

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APPENDIX A SURVEY FOR STUDY 1

Informed Consent Protocol Title: Innovativeness as a measure of lifecycle and multiple diffusion curves

Please read this consent document carefully before you decide to participate in this study.

Purpose of the research study This study is interested in your social media adoption habits as they relate to your friends and society at large while also measuring your innovativeness quotent. This survey will break into sections and ask you to consider your social media adoption as it relates to those individuals. Please take care to consider what comparisons each section asks.

This is a survey designed to assess if and when you adopted social media across several platforms: Twitter, Facebook, and Snapchat, and what motivations and other factors may have contributed to that adoption, or non-adoption.

Time required ~20 minutes

Risks and Benefits There are no risks associated with this survey. As a slight benefit, you may be more aware of your social media adoption habits.

Compensation You will be paid $1.25 compensation through Innovate for participating in this research.

Confidentiality Your identity will be kept confidential to the extent provided by law. Your information will be assigned a code number. Your name will not be used in any report.

Voluntary participation Your participation in this study is completely voluntary. There is no penalty for not participating.

Right to withdraw from the study You have the right to withdraw from the study at any time without consequence.

Who to contact if you have questions about the study Dennis DiPasquale, PhD Candidate, College of Journalism and Mass Communications, University of Florida, [email protected], 215-983-8870 Amy Jo Coffey, PhD, College of Journalism and Mass Communication, University of Florida, [email protected]. 352-392-6522

Who to contact about your rights as a research participant in the study IRB02 Office

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Box 112250 University of Florida Gainesville, FL 32611-2250 phone 392-0433.

Agreement I have read the procedure described above. By continuing to the next screen I voluntarily agree to participate in the procedure.

[end informed consent] [next page]

This study is interested in your social media adoption habits as they relate to your friends and society at large. This survey will break into sections and ask you to consider your social media adoption as it relates to those individuals. Please take care to consider what comparisons each section asks.

[next page]

For the first bank of questions, think about your social media adoption as it compares to your immediate group of friends.

Compared to my friends, I am a member of fewer social media services 1. In general, I am the last in my circle of friends to know about new social media services. 2. In general, I am among the first in my circle of friends to adopt a new social media service when it appears 3. If I heard about a new social media service that was available, I would be interested enough to try it 4. I will try a new social media service, even if none of my friends have tried it yet 5. I know about new social media services before other friends do

[next page]

For the next bank of questions, please think about how you adopt social media relative to the rest of the US population, regardless of age, interests or other factors that may be unique to you.

6. Compared to the rest of society at large, I am a member of fewer social media services 7. In general, I am the last person to know about new social media services 8. In general, I am among the first people to adopt a new social media service when it appears

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9. If I heard about a new social media service that was available, I would be interested enough to try it 10. I will try a new social media service, even if nobody else is using it 11. I know about new social media services before other people do

[next page]

For the following questions, please think to when you first adopted social media services. For Facebook, you are encouraged to log into the service and [insert instructions here for determining date of joining]

Adopting a service involves creating a profile, linking (friend requesting, or accepting a friend request) with friends, and regularly using the service in some capacity. If you feel you didn’t fully adopt a service when you officially joined, please consider the later time of adoption as defined here.

1. To the best of your knowledge, when did you adopt Facebook (early/mid/late) Year: ______or Never 2. When you adopted Facebook, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, with 1 being first to 7 being last) 3. To the best of your knowledge, when did you adopt Instagram (early/mid/late) Year: ______or Never 4. When you adopted Instagram, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last) 5. To the best of your knowledge, when did you adopt Snapchat (early/mid/late) Year: ______or Never 6. When you adopted Snapchat, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last) 7. To the best of your knowledge, when did you adopt LinkedIn (early/mid/late) Year: ______or Never 8. When you adopted LinkedIn, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last) 9. To the best of your knowledge, when did you adopt Twitter (early/mid/late) Year: ______or Never 10. When you adopted Twitter, how did that compare to your social group’s adoption habits (Likert scale, 1 to 7, first to last)

[next page]

Skip logic will be employed if someone indicates “never” for a particular service. On a laptop or desktop computer, please open Facebook and log into your account. View your profile and if you can’t see when you joined Facebook under your “Intro” section, hover your mouse/cursor over that and click the pencil icon. From there, you can scroll down to see when you adopted the service.

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21: When did you join Facebook according to the information shown by Facebook? [date entry]

Continue to twitter.com/username (substituting your actual username) to view your twitter profile, on the top left you can see when you joined twitter.

22: When did you join Twitter, according to the information shown by Twitter? [Date Entry]

[next page]

For the following questions, please indicate current demographic information. Please note that this survey would be interested in current information, but also certain demographics such as household income at the time of adoption of Facebook, SnapChat. and Twitter. If you do not wish to answer a particular question, please select the option for “Choose not to answer.”

23: Age: [number entry]

24: Gender: [drop down: Male, Female, Other]

25: Highest Education Level [Drop Down: Less than High School, High School, Some College, College Degree, Master’s Degree, Higher than Master’s Degree]

26: Annual Household Income Currently (if you’re still a dependent of your parents or guardian, please provide your best estimate) [drop down: under $20,000; $20,000- $34,999; $35,000-$49,999; $50,000-$74,999;$75,000 - $99,999; $100,000 - $149,999; $150,000+]

27: Current Marital Status: [Drop Down: Single/Never Married; Married/Civil Partnership; Divorced or Separated; Widowed]

28: How Many Children do you currently have? [number entry]

29: Employment Status Currently [Check Boxes: Unemployed, Employed less than 30 hours/week, employed at least 30 hours per week]

Note: Though similar, these questions are not to determine educational status, per se., and are different than the highest degree earned.

30: Student Enrollment Status Currently [Check Boxes: Not Enrolled, In High School, Part time undergraduate (as determined by your institution), Full Time Undergraduate, Part Time Graduate, Full Time Graduate, other ]

[next page]

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[skip logic if never adopted] For the demographic questions above, please take some time to think about your status at the time of Facebook adoption.

31: Highest Education Level when adopting Facebook: [Drop Down: Less than High School, High School, Some College - Undergraduate, Associates Degree, Bachelor’s Degree, Some Graduate, Master’s Degree, Higher than Master’s Degree]

32: Annual Household Income at the time of Facebook Adoption (if you were still a dependent of your parents or guardian, please provide your best estimate) [drop down: under $20,000; $20,000-$34,999; $35,000-$49,999; $50,000-$74,999;$75,000 - $99,999; $100,000 - $149,999; $150,000+]

33: Marital Status at the time of Facebook Adoption: [Drop Down: Single/Never Married; Married/Civil Partnership; Divorced or Separated; Widowed]

34: How Many Children did you have when adopting Facebook? [number entry]

35: Employment Status at the time of Facebook Adoption [Check Boxes: Unemployed, Employed less than 30 hours/week, employed at least 30 hours per week]

36: Student Enrollment Status at the time of Facebook [Check Boxes: Not Enrolled, In High School, Part time undergraduate (as determined by your institution), Full Time Undergraduate, Part Time Graduate, Full Time Graduate, other ]

[next page]

[skip logic if never adopted] For the demographic questions above, please take some time to think about your status at the time of Snapchat adoption.

37: Highest Education Level when adopting Snapchat: [Drop Down: Less than High School, High School, Some College - Undergraduate, Associates Degree, Bachelor’s Degree, Master’s Degree, Higher than Master’s Degree]

38: Annual Household Income at the time of SnapChat adoption(if you were still a dependent of your parents or guardian, please provide your best estimate) [drop down: under $20,000; $20,000-$34,999; $35,000-$49,999; $50,000-$74,999;$75,000 - $99,999; $100,000 - $149,999; $150,000+]

39: Marital Status at the time of SnapChat adoption: [Drop Down: Single/Never Married; Married/Civil Partnership; Divorced or Separated; Widowed]

40: How Many Children did you have when adopting SnapChat? [number entry]

41: Employment Status At the time of SnapChat Adoption [Check Boxes: Unemployed, Employed less than 30 hours/week, employed at least 30 hours per week]

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42: Student Enrollment Status At the time of SnapChat Adoption[Check Boxes: Not Enrolled, In High School, Part time undergraduate (as determined by your institution), Full Time Undergraduate, Part Time Graduate, Full Time Graduate, other ]

[next page]

[skip logic if never adopted] For the demographic questions above, please take some time to think about your status at the time of Twitter adoption.

43: Highest Education Level when adopting Twitter: [Drop Down: Less than High School, High School, Some College, Some College - Undergraduate, Associates Degree, Bachelor’s Degree, Master’s Degree, Higher than Master’s Degree]

44: Annual Household Income at the time of Twitter Adoption (if you were still a dependent of your parents or guardian, please provide your best estimate) [drop down: under $20,000; $20,000-$34,999; $35,000-$49,999; $50,000-$74,999;$75,000 - $99,999; $100,000 - $149,999; $150,000+]

45: Marital Status at the time of Twitter Adoption: [Drop Down: Single/Never Married; Married/Civil Partnership; Divorced or Separated; Widowed]

46: How Many Children did you have when adopting Twitter? [number entry]

47: Employment Status at the time of Twitter Adoption [Check Boxes: Unemployed, Employed less than 30 hours/week, employed at least 30 hours per week]

48: Student Enrollment Status at the time of Twitter Adoption [[Check Boxes: Not Enrolled, In High School, Part time undergraduate (as determined by your institution), Full Time Undergraduate, Part Time Graduate, Full Time Graduate, other ]

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APPENDIX B SURVEY FOR STUDY 3

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Again, this survey is looking at why people adopt social media. "Adoption" means creating a profile and using the service with some form of regularity.

For this next set of questions, we would like to know how you think you compare to the adoption habits of the United States population overall. While your adoption may have evolved over time, consider how you’d adopt if a new social media product came out today.

neither strongly mildly agree nor strongly disagree disagree disagree disagree mildly agree agree agree If I heard about a new social media service that was available, I O O O O O O O would be interested enough to try it In general, I am the last person to know about new social media O O O O O O O services In general, I am among the first people to adopt a new social O O O O O O O media service when it appears I will try a new social media service, even if nobody else is O O O O O O O using it I know about new social media O O O O O O O services before other people do Compared to the rest of society at large, I am a member of fewer O O O O O O O social media services

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Roughly how large is your group of friends or social circle? 1 to 5 ; 6 to 10 ;11 to 15 ; 16 to 20 ; 21 to 25 ; 26 or more

{Page Break}

Many of the questions will ask you about adopting social media. Again, this means creating a log-in and actively starting to use a service. That last part is key. If you created an account in 1995 but waited a year to actually start using it, the adoption would be 1996. For Facebook you can log in to your account to see when you created your account. For Twitter you can see this if you view your profile. For Instagram and Snapchat, your best estimation will be fine.

For each adoption, we want to know why you chose to adopt. For this, don't think about how you used it, but what you were hoping to be able to do, or what you wanted to get from using the service. There are no wrong answers.

{Page Break}

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{The following set of questions will be asked for Facebook, Instagram, Snapchat, and Twitter (SERVICE). The order is random}

When did you adopt SERVICE?

Drop Down: I have never adopted SERVICE; Each year for when SERVICE was available to public

Think about what made you finally decide to adopt.... Please, briefly tell us in the box below why you signed up and started to use SERVICE. Or you've never adopted, please let us know why.

What was your opinion of adopting Instagram when you started using SERVICE? (If you did not adopt, you can skip this question) Somewhat Neither Positive Somewhat Very Negative Negative Negative nor Negative Positive Positive Very Positive

Roughly how large was your group of friends or social circle when adopting Instagram? (If you did not adopt, you can skip this question) 1 to 5 ; 6 to 10 ; 11 to 15 ; 16 to 20 ; 21 to 25 ; 26 or more

{Page Break after each}

{The following set of questions will be asked for Facebook, Instagram, Snapchat, and Twitter (SERVICE). The order is random. Users are only shown the option if they indicated adoption of that service. To avoid frustration and fatigue, users are not forced to answer, so they only need to click “yes”}

SERVICE:

The following options may or may not reflect what you told us. For each option that applies, chose "yes" and tell us how well you thought SERVICE would help achieve that goal, drive, or directive. Feel free to select more than one. While some items seem like duplicates, they do mean different things to different people and both may apply to you. For each one you indicate, please select how strongly you felt the social media would do what you hoped it would do, and how important that item was to you.

Did this How well you thought How Important this factor into SERVICE would do was to you (1=not your this (1=poorly, important, 5=very decision to 5=very well) important) adopt? Yes No 1 2 3 4 5 1 2 3 4 5 I wanted to show who I am O O O O O O O O O O O O I wanted to see art/pictures/photos O O O O O O O O O O O O I wanted to connect with friends that were on it O O O O O O O O O O O O It seemed new/different/interesting O O O O O O O O O O O O I wanted to use it to play games O O O O O O O O O O O O It was a more private way to communicate O O O O O O O O O O O O I wanted to be in-tune with what was going on socially O O O O O O O O O O O O I wanted to find/connect old friends O O O O O O O O O O O O I wanted to share moments with people O O O O O O O O O O O O Nothing better to do O O O O O O O O O O O O I wanted to try it out O O O O O O O O O O O O I wanted to pass the time O O O O O O O O O O O O

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Did this How well you thought How Important this factor into SERVICE would do was to you (1=not your this (1=poorly, important, 5=very decision to 5=very well) important) adopt? Yes No 1 2 3 4 5 1 2 3 4 5 I wanted to see what other people were up to O O O O O O O O O O O O It was a good way to get news and/or information, O O O O O O O O O O O O I wanted to stay informed I wanted to know when things were happening O O O O O O O O O O O O I wanted to get to know people O O O O O O O O O O O O I wanted to be aware of events O O O O O O O O O O O O I wanted to show a different version of myself O O O O O O O O O O O O I wanted to keep in touch with people O O O O O O O O O O O O I wanted to keep up with friends O O O O O O O O O O O O I wanted to display group/club/cultural membership O O O O O O O O O O O O It was a better way to communicate O O O O O O O O O O O O It was a way to be with friends O O O O O O O O O O O O I didn’t want to be left out O O O O O O O O O O O O I wanted to have something to talk about O O O O O O O O O O O O I wanted to find new friends O O O O O O O O O O O O I wanted to use it to get away from reality or day-to-day life O O O O O O O O O O O O I wanted to see what people were talking about O O O O O O O O O O O O I wanted to interact with friends O O O O O O O O O O O O I wanted to post pictures O O O O O O O O O O O O It looked like fun O O O O O O O O O O O O I wanted to see how people change O O O O O O O O O O O O I wanted to find/join clubs or groups that appealed to me O O O O O O O O O O O O I wanted to see other places O O O O O O O O O O O O I wanted to create my identity O O O O O O O O O O O O I wanted to see things that interested me O O O O O O O O O O O O I liked taking pictures O O O O O O O O O O O O It was a more personal way to communicate O O O O O O O O O O O O I wanted to use it to discover new things O O O O O O O O O O O O It was easier to talk to distant friends/family O O O O O O O O O O O O I wanted to find and/or connect to classmates/co-workers O O O O O O O O O O O O I wanted to re-invent myself O O O O O O O O O O O O I wanted to keep up with what’s popular and/or follow trends O O O O O O O O O O O O I wanted to build friendships O O O O O O O O O O O O I didn’t want to miss out on anything O O O O O O O O O O O O I wanted to stay up-to-date on information O O O O O O O O O O O O I didn’t want to miss out on what other people were doing O O O O O O O O O O O O I wanted to discover who I am O O O O O O O O O O O O I wanted to follow famous people O O O O O O O O O O O O I was bored O O O O O O O O O O O O

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Did this How well you thought How Important this factor into SERVICE would do was to you (1=not your this (1=poorly, important, 5=very decision to 5=very well) important) adopt? Yes No 1 2 3 4 5 1 2 3 4 5 It was an easier way to chat or communicate O O O O O O O O O O O O I wanted to see what friends were posting O O O O O O O O O O O O I wanted to communicate with friends/family O O O O O O O O O O O O I wanted to be more social O O O O O O O O O O O O I wanted to be part of the in-crowd O O O O O O O O O O O O I was curious O O O O O O O O O O O O I wanted to know what was going on in the world O O O O O O O O O O O O I wanted to show people what’s going on in my life O O O O O O O O O O O O I wanted to find and/or connect to people in my field O O O O O O O O O O O O I wanted to network O O O O O O O O O O O O I didn’t want to be the odd-one-out O O O O O O O O O O O O I wanted to see what other people were seeing O O O O O O O O O O O O I wanted to be involved with friends O O O O O O O O O O O O I wanted to use it to access games or other sites O O O O O O O O O O O O

For the following questions, we are looking to understand what, if any, pressure you felt to adopt from other individuals. This pressure does not necessarily need to have a negative connotation to it, it simply means you had one or several individuals suggesting you join. For each one, Select "yes" and rate the level of pressure from that person and your motivation or desire to follow do as they requested.

Rate how How strong the motivated This pressure were you to

applies was do what this (1=low, person 5=high) wanted? Yes No 1 2 3 4 5 1 2 3 4 5 one or more friends O O O O O O O O O O O O one or more family member O O O O O O O O O O O O one or more classmate/coworker O O O O O O O O O O O O one or more parents or guardians O O O O O O O O O O O O a superior at a job/school or some O O O O O O O O O O O O authority not part of your family

{Page Break after each}

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For the following questions, please indicate current demographic information. If you do not wish to answer a particular question you can skip it. {all options are drop down}

Age (drop down): Options are blank, 18 through 99 offered individually, 100 or older. No individuals under 18 should be included in the survey.

Gender Male Female Other

Highest Education Level Less than High School ; High school graduate ; Some college ; 2 year degree ; 4 year degree ; Master's Degree ; Professional degree ; Doctorate

Annual Household Income If you are still a dependent of your parents or guardian, please provide your best estimate under $20,000 ; $20,000 - $34,999 ; $35,000 - $49,999 ; $50,000 - $74,999 ; $75,000 - $99,999 ; $100,000 - $149,999 ;$150,000 +

Current Marital Status Single/Never Married Married/Civil Partnership Divorced/Separated Widowed/Widower

How many children do you have 0 1 2 3 4 5 6 7 8 9 10 or more

Employment Status: Unemployed Employed less than 30/hours per week (Part Time) Employed at least 30 hours per week (Full Time)

Are you currently enrolled as a student? Not Enrolled ; Enrolled in high school or similar program ; enrolled in a 2 or 4-year undergraduate degree program ; enrolled in a master's degree program ; enrolled in a doctorate degree program ; enrolled in a technical, professional, or certificate program

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BIOGRAPHICAL SKETCH

Dennis DiPasquale earned a Bachelor of Science degree in computer science from Rowan University in 1999. After working in information technology, he changed fields to design and eventually marketing communication through the 2000s. In 2008 he earned a Master of Arts in public relations from Rowan University. From 2003 to 2010 he maintained Kenazz Communications, a consultancy aiding his clients in the areas of communication arts and strategy. He also spent time as an executive at a mid-sized credit union directing advertising and brand strategy for a year. Dennis has been a

Lecturer of Marketing at the University of Florida since 2015. He earned his Ph.D. in

Mass Communication from the University of Florida in the Fall of 2017.

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