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Why Do People Continue Using Facebook: An Empirical Study Why Do People Continue Using Facebook: An Empirical Study

From the Perspectives of Technology Adoption and Social From the Perspectives of Technology Adoption and Social

Contract Contract

Chen-Wei Chang University of Southern Mississippi

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The University of Southern Mississippi

WHY DO PEOPLE CONTINUE USING FACEBOOK: AN EMPIRICAL STUDY

FROM THE PERSPECTIVES OF TECHNOLOGY ADOPTION

AND SOCIAL CONTRACT

by

Chen-Wei Chang

Abstract of a Dissertation

Submitted to the Graduate School

of The University of Southern Mississippi

in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

May 2016

ii

ABSTRACT



AND SOCIAL CONTRACT

by Chen-Wei Chang

May 2016

Online stalking, identity theft, and other privacy-related issues have become the

major reasons that impede users from continuously using their Facebook accounts. To

better understand how privacy risks, among other factors, have come into play, in terms

of affecting users’ intention to continue using social networking sites, the present study

applies three theories (i.e., the unified theory of acceptance and use of technology 2,

social contract theory, and technology continuance theory) to develop a new model for

Facebook use continuance. An online survey (N = 450) was performed by administrating

a random sampling method in January and February of 2014. Data analysis employing

structural equation modeling (SEM) shows that the examined predictors (i.e.,

performance expectancy, facilitating conditions, hedonic motivation, trust, perceived

risks, attitude, and satisfaction) are accountable for the intention to continue using

Facebook, with the entire model explaining 65% of the variance. Theoretical

ramifications for future research and practical implications for social media companies

and marketers are also discussed.

COPYRIGHT BY

CHEN-WEI CHANG

2016



AND SOCIAL CONTRACT

by

Chen-Wei Chang

A Dissertation

Submitted to the Graduate School

and the Department of Mass Communication and Journalism

at The University of Southern Mississippi

in Partial Fulfillment of the Requirements

for the Degree of Doctor of Philosophy

Approved:

________________________________________________

Dr. Fei Xue, Committee Chair

Associate Professor, Mass Communications and Journalism

________________________________________________

Dr. Christopher P. Campbell, Committee Member

Professor, Mass Communication and Journalism

________________________________________________

Dr. Jae-Hwa Shin, Committee Member


________________________________________________

Dr. Mary Lou Sheffer, Committee Member

Associate Professor, Mass Communication and Journalism

________________________________________________

Dr. David R. Davies, Committee Member


________________________________________________

Dr. Karen S. Coats

Dean of the Graduate School

May 2016

iii

ACKNOWLEDGMENTS

Writing and completing a dissertation are never an easy task. Thanks to my

committee members and professors, I was able to overcome the difficulties I encountered

during the process of writing, data analyses, and chapter organizations.

First and foremost I would like to thank Dr. Fei Xue, serving as my dissertation

chair and research mentor during my study at the University of Southern Mississippi. Dr.

Xue was always available and willing to answer my questions whenever I encountered

challenges or needed suggestions for my research. I would also like to give special thanks

to Dr. Christopher Campbell who was always very supportive of my dissertation. Each

time I had a conversation with Dr. Campbell, he asked if I needed additional technology

or software for the data analyses. Thanks Dr. Mary Lou Sheffer for providing suggestions

and critiques for my survey and research design. Without Dr. Sheffer’s help, I would not

have been able to recognize the misplacement of “attitude” and “satisfaction” in my

prospectus. I appreciate Dr. Jae-Hua Shin and Dr. David R. Davies, both of them agreed

to join my committee without any hesitation during the time I almost finished the data

analyses. Reforming dissertation committee can be challenging, but Dr. Shin and Dr.

Davies were both very understandable and supportive to my dissertation topic.

Last but not the least, I would like to appreciate Dr. Jun Heo and Dr. Gina

Massulo Chen, both of them served as the committee members for my prospectus before

leaving our school. Dr. Jun Heo taught me how to use IBM SPSS Amos as well as apply

structural equation modeling to my research. Dr. Gina Massulo Chen helped me solve

some of the writing issues in my prospectus. Without their kind assistance, I would not

have been able to put this dissertation together.

iv

TABLE OF CONTENTS

ABSTRACT ........................................................................................................................ ii

ACKNOWLEDGMENTS ................................................................................................. iii

LIST OF TABLES ............................................................................................................. vi

LIST OF ILLUSTRATIONS ............................................................................................ vii

CHAPTER

I. INTRODUCTION ...................................................................................1

Background of the Problem

Purpose of the Study

Significance of the Study

II. LITERATURE REVIEW .........................................................................5

Facebook and Its Privacy Controversies

Theoretical Background

The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

Social Contract Theory

Technology Acceptance Model

III. RESEARCH HYPOTHESES AND MODEL ........................................20

Attitude, Satisfaction, and Continuance Intention

Exogenous Predictors and Their Effects on Attitude and Satisfaction

IV. METHOD ................................................................................................26

Survey Research

Sample

Procedure

Instrumentation

V. RESULTS ...............................................................................................34

Descriptive Statistics

Validity and Reliability Measures

Examinations of Model Fit

Hypotheses Testing

Mediation Analysis

v

VI. DISCUSSION .........................................................................................47

Discussion of Results

Theoretical Ramifications

Practical and Marketing Implications

VII. Limitations and Future Research .............................................................53

APPENDIXES ...................................................................................................................56

REFERENCES ..................................................................................................................69

vi

LIST OF TABLES

Table

1. Core Constructs and Root Constructs/ Theories for UTAUT ..................................9

2. Added Constructs and Root Studies for UTAUT2 ................................................. 11

3. Core Constructs and Root Constructs/ Theories for TCT ......................................18

4. Descriptive Demographic Statistics .......................................................................27

5. Descriptive Statistics ..............................................................................................35

6. Convergent Validity ...............................................................................................39

7. Discriminant Validity .............................................................................................40

8. Fit Indices for the Measurement Model .................................................................41

9. Hypotheses Testing ................................................................................................44

10. Bootstrap Values for Direct, Indirect, and Total Effect ..........................................45

vii

LIST OF ILLUSTRATIONS

Figure

1. Unified Theory of Acceptance and Use of Technology (UTAUT) ........................ 11

2. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) ............13

3. Technology Continuance Theory (TCT) ................................................................19

4. Research Model .....................................................................................................25

5. Measurement Model in IBM SPSS Amos 22 ........................................................38

6. Structural Model in IBM SPSS Amos 22 ..............................................................43

7. Structural Model ....................................................................................................44

1

CHAPTER I

INTRODUCTION

Background of the Problem

In the past decade, social media has become a major part of people’s everyday

lives. According to Experian Marketing Services, users in the United States spend 16

minutes out of every hour on social media (Tatham, 2013). Among the various online

platforms, Facebook makes up 83% of the total time Americans spend on

social-networking sites (Leonard, 2013); therefore, Facebook has been ranked the most

popular social media platform from among the many other competitors in the market (e.g.,

Twitter, LinkedIn, and Pinterest). As of September 30, 2012, Facebook had more than

166 million users in the United States, meaning that one out of every two Americans had

a Facebook account (Internet World Stats, 2012). Because of its high penetration rate,

Facebook has not only played an important role in users’ everyday lives, but has also

become one of the most efficient and effective marketing tools.

Since Facebook requires its users’ personal information to provide customized

services (e.g., suggestions of friends and fan pages, location-based services, etc.),

collecting private personal data and tracking a user’s online activities are necessity for

Facebook (Facebook Terms and Policies, 2013). On the other hand, advertising is

Facebook’s major revenue source, accounting for $1.33 billion earned in the fourth

quarter of 2012 (Cutler, 2013). Because most advertising on Facebook targets certain

audiences, the amount of user-related information Facebook can collect from its users

significantly impacts the revenue Facebook gets from targeted marketing and advertising.

Although revealing personal information seems natural to most Facebook users, online

2

stalking, identity theft, harassment, and other privacy-related issues have gradually

become major concerns that impede users from continuously using their Facebook

accounts. For example, Stieger, Burger, Bohn, and Voracek’s (2013) study found that

nearly half the users (48%) who quit their Facebook accounts did so because of privacy

concerns—the number one reason that users deactivated their Facebook pages. There is

an urgent need for Facebook, as well as other social media organizations, to understand

how to keep their existing users, and how privacy-related issues, among other factors, can

come into play to affect users’ continued use of social networking sites. This study would

therefore attempt to enrich the literature in this field by proposing a new behavior model

explaining users’ intention to continue using Facebook.

Purpose of the Study

The present study aims to answer the following two overarching questions: 1)

What are the reasons behind Facebook continuance? and 2) How do different factors

explain such intention? This study administered an online survey to address these issues.

Three theories are utilized to create a new behavior model: The unified theory of

acceptance and use of technology 2 (UTAUT2; Venkatesh, Thong, & Xu, 2012), social

contract theory (Milne & Gordon, 1993), and technology continuance theory (TCT; Liao,

Palvia, & Chen, 2009). The constructs from UTAUT2 (i.e., performance expectancy,

effort expectancy, social influence, facilitating conditions, hedonic motivation) and social

contract theory (i.e., trust and perceived risks) are examined as exogenous predictive

variables. Satisfaction and attitude, the precursors for behavioral intention suggested by

TCT, are examined as endogenous predictive variables. This study investigates the

various predictors’ influence on a user’s continued intention to use Facebook, the

3

outcome variable for the proposed behavior model. Therefore, the purpose of this study is

four-fold: 1) It investigates the proposed model’s explanatory ability for predicting users’

intention to continue using Facebook; 2) It examines the effects of users’ attitude toward

and satisfaction with Facebook on their continuance intention, as well as examining the

precursors of attitude and satisfaction; 3) It probes each exogenous predictive variable’s

influence on the gauged intention, mediated through users’ attitude toward and

satisfaction with Facebook; and, 4) It provides theoretical ramifications for future

research, as well as practical implications for social media companies and marketers, by

discussing a strategy for keeping existing users active on the sites.

Significance of the Study

The significance of this study is both theoretical and practical. First, social media

use is different from traditional media consumption in that social media use requires a

user’s active participation to create user-generated content and online interaction (Gallant

& Boone, 2011). Because of this participation, social media use, known as a form of

computer-mediated communication, is considered a behavior relying more on

information and communication technology (ICT) than on the use of traditional media.

This leads to the present study’s intention to adapt theories from the field of ICT. Because

UTAUT2 has been empirically verified to have the highest predictive ability toward

users’ adoption of ICT compared to other technology acceptance models (Venkatesh et al.,

2012), examining how UTAUT2 can be applied to Facebook use would lead to a

significant theoretical contribution in the field of social media research. Additionally,

because online stalking, identity theft, harassment, and other privacy-related issues have

become the major reason why users discontinue their Facebook accounts in recent years

4

(Stieger et al, 2013), the present study regards the use of Facebook as a risky behavior by

incorporating the concepts of trust and perceived risks from social contract theory to

better understand users’ intention to continue using Facebook. Furthermore, previous

studies have explored various online platforms and proposed insightful arguments

regarding the factors influencing users’ adoption of social media (e.g., Hargittai, 2007;

Hargittai & Litt, 2011). However, as Facebook’s penetration rate rises, retaining existing

users instead of attracting newer ones is considered a more significant agenda for

Facebook. Early studies on Facebook continuance either do not comprehensively

examine aforehand mentioned factors or simply focus on small Facebook markets. For

example, Hsu and Wu (2011) and Wu, Huang, and Hsu (2014) explore Facebook

continuance in Taiwan, yet their studies falls short of a small Facebook population and

snowball sampling method. For a better understanding of continuance intention,

technology continuance theory (TCT) suggests that attitude and satisfaction are the two

precursors of users’ intentions to continue using ICT (Liao et al., 2009). Previous

literature also found that users’ attitude and satisfaction could serve as the outcomes for

the predicted variables proposed by this study (for more details, see research hypotheses

and model). Therefore, the mediating role of users’ attitude and satisfaction are

investigated to provide in-depth information for theoretical and practical implications.

5

CHAPTER II

LITERATURE REVIEW

Facebook and Its Privacy Controversies

Facebook, one of the most popular social networking sites, was launched by Mark

Zuckerberg in 2004, then a sophomore at Harvard University (Nicholas, 2010). At first,

Facebook only accepted membership registrations from Harvard University students, and

it later expanded to general users and soon became one of the most popular social media

outlets (Nicholas, 2010). In May 2008, Facebook surpassed MySpace as the most visited

social networking site worldwide (McCarthy, 2008). In October 2009, Facebook again

beat MySpace to become the most popular social networking site in the United States

(Smith, 2009). As of the first quarter of 2013, Facebook reported that there are 1.19

billion global Facebook users (Facebook, 2013). According to Experian Marketing

Services, online users in the United States spend 16 minutes out of every hour on social

media (Tatham, 2013), of which Facebook makes up 83% of the total time Americans

spend on social-networking sites (Leonard, 2013). As of September 30, 2012, Facebook

had more than 166 million users in the United States, meaning that one out of every two

Americans had a Facebook account (Internet World Stats, 2012). This high penetration

rate has made Facebook an indispensible part of Americans’ everyday lives.

Facebook tracks users’ online activities and requires users to voluntarily disclose

personal information to provide customized service such as suggestions of friends and fan

pages, location-based services, etc. (Chang & Heo, 2014; Facebook Terms and Policies,

2013). For example, Facebook’s “nearby places” function provides its users with

information about nearby stores based on users’ specific geographical locations. There is

6

no service fee required from Facebook users because Facebook also utilizes users’

information for targeted advertising and marketing. According to Cutler (2013),

advertising is Facebook’s major revenue source, accounting for $1.33 billion earned in

the fourth quarter of 2012. Since most advertising on Facebook targets certain audiences,

the amount of user-related information Facebook collects severely impacts the revenue

Facebook can get from targeted marketing and advertising. This business mechanism has

been proven successful because Facebook has become one of the most efficient and

effective marketing tools in recent years. However, Facebook has faced a major challenge

with regard to privacy-related issues, and this challenge has further led to both users’

reluctance to use Facebook and the discontinuance of their existing Facebook accounts

(Stieger et al., 2013). Because active users are considered to be Facebook’s major asset,

this problem has caused an unprecedented crisis for this social media giant.

Even though Facebook claims it only utilizes users’ information for advertising

and marketing purposes, security malfunctions, which could possibly compromise users’

information safety, have been regularly reported. For example, a severe security glitch

that released users’ personal information to unauthorized personnel was discovered on

Facebook in 2010 (Wortham, 2010). This has made Facebook users vulnerable to online

predators, harassment, identity theft, and other potential risks. For example, Facebook’s

location-based service may disclose users’ physical locations, providing burglars with the

opportunity to break into users’ houses. Furthermore, Facebook utilizes users’ personal

information for advertising, even though it may cause potential risks for its users. For

example, Working to Halt Online Abuse (WHOA; 2009) reported that cyber-stalking on

Facebook threatens young adults and female users. Nevertheless, Facebook allows

7

teenagers’ status postings to be viewed by the general public to gain more profit from

advertisers targeting young Facebook users (Kelly, 2013). Perhaps most importantly,

Facebook shares its users’ personal information with third parties to boost its profit,

although Facebook claims that, in doing so, it aims to provide a better personalized

experience (Pegoraro, 2010). In this respect, if users log into a third party’s website by

using their Facebook accounts (i.e., users “link” their Facebook accounts with the third

party’s website), their Facebook friends logging into the same site later on could possibly

see the users’ relevant activities (e.g., news users have read, music users have listened to,

products users have purchased, etc.; Wolverton, 2010). Thus, Facebook users’

information is at risk of being improperly used, which could lead to potential property

loss and endangerment of personal lives.

Because of the privacy-related issues, Facebook has continued to regularly update

its privacy settings. For example, Facebook users have been able to decide who can see

their individual postings on their profile pages (i.e., “Everyone,” “friends of friends,”

“friends only,” and “only me”) since 2009. In May 2010, Facebook further provided its

users with a more user-friendly interface for privacy settings. Even though Facebook tried

hard to improve its privacy protection, its regular technological glitches and the sharing

of personal information for targeted advertising make users’ privacy concerns inevitable.

Consequently, privacy-related issues on Facebook have become the major reason leading

to users’ discontinuance of their Facebook accounts (Smith, 2010). Stieger and

colleagues’ (2013) study found that nearly half of the users (48%) who quit their

Facebook accounts did so because of privacy concerns—the number one reason for users

deactivating their Facebook pages. In this respect, there is an urgent need for Facebook

8

and other social media companies to understand how to keep their existing users, and

how privacy-related issues, among other factors, can come to affect users’ intention to

continue using social media. Although privacy-related issues on Facebook have recently

received a lot of attention from the public, the surprisingly limited development of

research in this field requires more exploration (Piotrowski, 2012). This study would

therefore attempt to enrich the literature in this field by proposing a new behavior model

for explaining users’ intention to keep using Facebook.

Theoretical Background

Social media use is different from traditional media consumption in that social

media use requires users’ active participation to create user-generated content and online

interaction (Gallant & Boone, 2011). Because of this reason, social media use, known as

a form of computer-mediated communication, is considered to be a behavior relying more

on ICT than the use of traditional media. This leads to the present study’s intention to

adapt theories from the field of ICT. Because UTAUT2 has been empirically verified to

have the highest predictive ability toward users’ adoption of ICT compared to other

technology acceptance models (Venkatesh et al., 2012), this study examined how

UTAUT2 can be also applied to the field of social media research.

The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

UTAUT2 (Venkatesh et al., 2012) are extensions of UTAUT, proposed by

Venkatech, Morris, G. B. Davis and F. D. Davis in 2003. UTAUT is a combination of

eight behavior models and theories that have been frequently used to examine users’

adoption of ICT: the theory of reasoned action (TRA), the technology acceptance model

(TAM), the motivational model (MM), the theory of planned behavior (TPB), the

9

combined theory of planned behavior/technology acceptance model (C-TPB-TAM), the

model of personal computer use (MPCU), the innovation diffusion theory (IDT), and the

social cognitive theory (SCT; Venkatech et al., 2003). By empirically examining users’

adoption of information technology (IT) in an organizational context, Venkatech, Morris,

Davis and Davis found that UTAUT predicts 70% of users’ adoption intentions and 50%

of their adoption behavior, which are better results than other existing technology

acceptance models. A decade after UTAUT was first introduced to academia, Venkatesh,

Morris, Davis and Davis’s article was ranked the second most cited paper in MIS

Quarterly (Venkatesh, 2012). The core constructs for UTAUT are all borrowed and

developed from the previously mentioned eight models and theories (see Table 1).

Table 1

Core Constructs and Root Constructs/ Theories for UTAUT

Core Constructs Definition Root Constructs/Theories

Performance

Expectancy

“The degree to which an individual

believes that using the system will

help him or her to attain gains in job

performance” (p.447).

1. Perceived Usefulness

(TAM/TAM2/C-TAM-TPB)

2. Extrinsic Motivation (MM)

3. Job-fit (MPCU)

4. Relative Advantage (IDT)

5. Outcome Expectations (SCT)

Effort Expectancy “The degree of ease associated with

the use of the system” (p.450).

1. Perceived Ease-of-Use

(TAM/TAM2)

2. Complexity (MPCU)

3. Ease-of-Use (IDT)

Social Influence “The degree to which an individual

perceives that important others believe

he or she should use the new system”

(p.451).

1. Subjective Norm (TRA, TAM2,

TPB/DTPB, and C-TAM-TPB)

2. Social Factors (MPCU)

3. Image (IDT)

Facilitating Conditions “The degree to which an individual

believes that an organizational and

technical infrastructure exists to

support use of the system” (p.453).

1. Perceived Behavioral Control

(TPB/DTPB, C-TAM-TPB)

2. Facilitating Conditions (MPCU)

3. Compatibility (IDT)

Note: More details can be found from Venkatesh et al. (2003). User acceptance of information technology: Toward a

unified view, MIS Quarterly, 27(3), 425-478.

10

The UTAUT model has been empirically verified and consists of several

predictive relationships and moderators:

1. Users’ performance expectancy, effort expectancy, and social influence predict users’

intention in adopting IT, whereas users’ facilitating conditions and adoption intention

predict their actual IT use (Venkatech et al., 2003). This primitive UTAUT model has

been widely extended to examine ICT adoption by researchers from different fields

(e.g., Lin & Anol, 2008; Wang & Wang, 2010; Wang, Wu, & Wang, 2009).

2. When four moderators (i.e., gender, age, experience, and voluntariness to use) were

further added to the primitive UTAUT model, UTAUT can explain 70% of the

variance regarding users’ adoption intention (Venkatech et al., 2003).

3. Regarding the moderating variables for UTAUT, users’ age and gender both moderate

the effects of users’ performance expectancy, effort expectancy, and social influence

on users’ adoption intention; users’ experience moderates the effects of users’ effort

expectancy and social influence on users’ behavior intention; and, users’ voluntariness

to use new IT moderates the effect of users’ social influence on their adoption

intention (Venkatech et al., 2003). Moreover, the effect of users’ facilitating

conditions on users’ adoption intention is moderated by users’ age and experience

(Venkatech et al., 2003; for UTAUT model, see Figure 1).

11

Effort

Expectancy

Social

Influence

Facilitating

Condition

Age Gender Experience Voluntariness

of Use

Behavioral

Intention Use Behavior

Performance

Expectancy

Figure 1. Unified Theory of Acceptance and Use of Technology (UTAUT).

UTAUT has been verified to have a high predictive power towards users’ IT

adoption in an organizational context and has been applied and extended to other contexts.

For example, Hsu and Wu (2011) and Wu, Huang, and Hsu (2014) extend UTAUT to

examine Facebook continuance in Taiwan. Their findings suggest that performance

expectance, effort expectancy, social influence, and facilitating conditions are the

predictors for Facebook continuance intention. However, Venkatesh and colleagues (2012)

believed it can be problematic if researchers directly apply UTAUT to examine

consumers’ adoption of ICT because of the difference of users’ roles as employer and

consumer. Therefore, three additional predictors (i.e., hedonic motivation, price value,

and habit) were added to the UTAUT2, based on the literature on consumer research, to

examine general users’ adoption of ICT (Venkatesh et al., 2012; see Table 2).

12

Table 2

Added Constructs and Root Studies for UTAUT2

Constructs Definition Root Studies

Hedonic Motivation “The fun or pleasure derived from using a

technology” (p.161).

1. Brown and Venkatesh (2005)

2. Childers et al. (2001)

3. Thong et al. (2006)

4. Van der Heijden (2004)

Price Value “Consumers’ cognitive tradeoff between

the perceived benefits of the applications

and the monetary cost for using them”

(p.161).

1. Chan et al. (2008)

2. Dodds et al. (1991)

3. Zeithaml (1988)

Habit “The extent to which people tend to

perform behaviors automatically because

of learning” “Scholars also equate habit

with automaticity” (p.161).

1. Ajzen (2002)

2. Ajzen & Fishbein (2005)

3. Kim & Malhotra (2005)

4. Limayem et al. (2007)

5. Ouellette & Wood (1998)

Note: More details can be found from Venkatesh et al. (2012). Consumer acceptance and use of information technology:

Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157-178.

UTAUT2 was empirically verified and suggests the following predictive

relationships for its constructs:

1. All seven predictive variables (i.e., users’ performance expectancy, effort expectancy,

social influence, facilitating conditions, hedonic motivation, price value, and habit)

influence users’ intention in adopting ICT (Venkatesh et al., 2012).

2. Users’ facilitating conditions, habit, and adoption intention directly predict their

adoption behavior (Venkatesh et al., 2012).

Three individual differences (i.e., age, gender, experience) are the moderators for

UTAUT2, after deleting “voluntariness of use” from UTAUT. This is mainly because

employees in an organization are often forced to adopt new ICT to increase the workplace

efficiency, whereas consumers use new ICT of their own free will (Venkatesh et al.,

2012). Hence, the following moderating effects are suggested by UTAUT2:

13

1. Users’ age and gender both moderate the effects of all seven predictive variables (i.e.,

users’ performance expectancy, effort expectancy, social influence, facilitating

conditions, hedonic motivation, price value, and habit) on users’ adoption intention

(Venkatesh et al., 2012).

2. Users’ experience only moderates the effects of five predictive variables (i.e., users’

effort expectancy, social influence, facilitating conditions, hedonic motivation, and

habit) on their adoption intention (Venkatesh et al., 2012).

3. The effect of users’ facilitating condition on users’ adoption behavior is moderated by

users’ age and experience (Venkatesh et al., 2012).

4. The effect of users’ habit on their adoption behavior is moderated by all three

moderators (i.e., users’ age, gender, and experience; Venkatesh et al., 2012).

5. The effect of users’ behavioral intention on their adoption behavior is moderated by

users’ experience (Venkatesh et al., 2012; see Figure 2).

14

UTAUT

UTAUT2

Performance

Expectancy

Effort

Expectancy

Social

Influence

Facilitating

Condition

Hedonic

Motivation

Price Value

Habit

Age Gender Experience

Behavioral

Intention Use Behavior

Figure 2. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2)

UTAUT2 has not only been extended to different types of ICT adoption (e.g.,

Huang, Kao, Wu, & Tzeng, 2013; Slade, Williams, & Dwivedi, 2013), but also been used

to examine users’ ICT adoption under diverse cultural contexts (e.g., Alawan, Dwivedi, &

Williams, 2013; Chong & Ngai, 2013). Although UTAUT2 has been widely applied by

behavioral science researchers from different fields, there has been no journal publication

utilizing UTAUT2 in the examination of the adoption of social media. Because social

media use, known as a form of computer-mediated communication, relies more on

technology than on the consumption of traditional media, incorporating the major

predictors from UTAUT2 to the proposed model would provide a significant theoretical

contribution in this field of social media research. Perhaps most importantly, social media

plays an important role in modern advertising/marketing; therefore, this study would also

15

provide useful implications for social media companies. To make the analysis more

manageable, the present study only used five predictive variables (i.e., users’ performance

expectancy, effort expectancy, social influence, facilitating conditions, and hedonic

motivation) and excluded the influences from moderators suggested by UTAUT2. “Price

value” was withdrawn from the proposed model since Facebook does not charge a fee

from its users; “habit” was also eliminated because of a lack of literature support.


Social contract theory is also applied for the model construction based on prior

literature on Internet online social community (Fogel & Nehmad, 2009). This is because

online stalking, identity theft, harassment, and other privacy-related issues have become

the major reason for why users discontinue their Facebook accounts in recent years

(Stieger et al., 2013). The present study therefore regards the use of Facebook as a risky

behavior, different from general technology adoption, by including social contract theory

as its theoretical framework.

Social contract theory is a political philosophy that is used to explain the

relationship among the individual, government, and society (Locke, 2007; Macneil, 1974;

Riley, 2006; Rousseau, 1971). Social contract theory assumes that the individual is

rational and understands that his/her unlimited freedom in our society may pose a risk of

harm to others; in this respect, the individual voluntarily gives up certain degrees of

freedom (e.g., not breaking the laws and regulations made by the government) in

exchange for a better social order (Rachels & Rachels, 2011; Rousseau, 1971). This kind

of voluntary consent is therefore considered a “social contract,” ensuring the benefit to

our society and the individuals living within it (Locke, 2007; Macneil, 1974; Riley, 2006;

16

Rousseau, 1971). Previous studies have applied social contract theory to examine the

“implicit social contract” (Milne & Gordon, 1993, p.207), a relative concept compared to

the “explicit (legal) social contract (i.e., law),” between marketers/advertisers and

consumers (e.g., Milne & Gordon, 1993; Okazaki, Li, & Hirose, 2009). Based on the

assumption that businesses and firms provide advantages to our society (Quelch & Jocz,

2008), the implicit social contract implies that consumers would voluntarily provide

personal or private information for services if marketers/advertisers keep their promise to

use consumers’ information for proper purposes only (Chang & Heo, 2014; Milne &

Gordon, 1993). In this respect, consumers’ trust toward marketers/advertisers, in terms of

whether they would behave in a responsible way, plays an important role in consumers’

voluntary consent for this implicit social contract (Okazaki et al., 2009). Moreover, when

providing personal information in exchange for services, consumers perceive not only

benefits from the services, but also risks from unexpected problems (Fogel & Nehmad,

2009; McKnight & Lankton, 2010; Quelch & Jocz, 2008). Culnan and Armstrong’s (1999)

study suggests that consumers would provide information in exchange for services only

under the circumstances that their perception of the benefits is higher than the risks

(Chang & Heo, 2014). Additionally, consumers’ perceived risks can be mitigated if their

level of trust is high (Krasnova, Spiekermann, Koroleva, & Hildebrand, 2010). In other

words, consumers’ trust, perceived benefits, and perceived risks all play a role in

affecting their voluntary consent for this implicit social contract.

Based on the assumptions of social contract theory, this study regards Facebook

users’ voluntary disclosure of personal information in exchange for social networking

services as a form of implicit social contract. When using Facebook, users are required to

17

provide personal information and allow the Facebook server to track their online

activities for providing customized services (e.g., suggestions of friends and fan pages,

location-based services, etc.; Facebook Terms and Policies, 2013). In this respect, users

trust that Facebook will behave properly and protect their personal information from

unauthorized use. However, if Facebook fails to keep its promise, consumers would be

less likely to trust Facebook, leading to their reluctance to provide personal information.

Previous studies have provided empirical evidence supporting the postulation that users’

trust affects their information disclosure on the web (e.g., Hoffman, Novak, & Peralta,

1999; Metzger, 2004). Trust is also considered to be the precursor of other kinds of social

contracts such as online transactions (Jarvenpaa & Tractinsky, 1999). Furthermore, users

understand that the disclosure of personal information, on one hand, guarantees a better

user experience on Facebook and, on the other hand, may also cause potential problems

such as online stalking, identity theft, harassment, and other privacy issues (Ibrahim,

2008; McKnight & Lankton, 2010; Tufekci, 2008). A previous study has found empirical

evidence that users’ privacy concern on Facebook is one of the significant precursors

predicting his or her discontinuance of Facebook use (Stieger et al., 2013). Krasnova,

Spiekermann, Koroleva, and Hildebrand (2010) also suggest Facebook users’ perceived

risks affect their willingness to disclose personal information. Consequently, this study

borrows the constructs of users’ trust and perceived risks from social contract theory for

the model construction. Because the construct of perceived benefits is very similar to

performance expectancy from UTAUT2, the researcher decided not to include it in the

research model.

18

Technology Continuance Theory (TCT)

Previous studies have explored various online platforms and proposed insightful

arguments regarding the factors influencing users’ adoption of social media (e.g.,

Hargittai, 2007; Hargittai & Litt, 2011). However, as Facebook’s penetration rate rises,

retaining existing users, instead of attracting newer ones, is considered a more significant

agenda for Facebook. For example, Hsu and Wu (2011) and Wu, Huang, and Hsu (2014)

explore Facebook continuance in Taiwan, yet their studies falls short of a small Facebook

population and snowball sampling method. For a better understanding of continuance

intention, technology continuance theory (TCT) was also applied for the model

construction in this study. TCT was proposed by Liao, Palvia, and Chen’s (2009) study on

information system adoption and continuance. It is an integrated model, incorporating the

technology acceptance model (TAM), the expectation confirmation model (ECM), and

the cognitive model (COG; Liao et al., 2009). The core constructs for TCT are all

borrowed and developed from the previously mentioned three models (see Table 3).

Table 3

Core Constructs and Root Constructs/ Theories for TCT


Perceived Usefulness “The prospective user’s subjective probability that

using a specific application system will increase

job performance (Davis, Bagozzi, & Warshaw,

1989, p. 985)” (p.310).

Technology Acceptance

Model (TAM)

Perceived

Ease-of-Use

“The degree to which the prospective user expects

the target system to be free of effort (Davis et al.,

1989, p. 985)” (p.310).

Technology Acceptance

Model (TAM)

19

Table 3 (continued).


Confirmation “Disconfirmation is defined as the discrepancy

between a user’s pre-adoption expectations and

perceived performance (Churchill & Suprenant,

1982; Oliver, 1980). The polarity of

disconfirmation is positive when the perceived

performance is higher than pre-adoption

expectations and the user is satisfied, or is negative

when perceived performance falls short of

expectations and the user is dissatisfied” (p.311).

Expectation Confirmation

Model (ECM)

Satisfaction “An individual’s post-consumption evaluation of a

specific transaction (Bolton & Drew, 1991; Hunt,

1977)” (p.311).

Cognitive Model (COG)

Attitude “An individual’s overall evaluation of a product or

service offering (Bolton & Drew, 1991; Hunt,

1977)” (p.311).

Cognitive Model (COG)

Note: More details can be found from Liao et al. (2009). Information technology adoption behavior life cycle: Toward a

Technology Continuance Theory (TCT). International Journal of Information Management, 29(4), 309-320.

Based on the empirical data on technology continuance, the TCT model proposes

the following assumptions:

1. Users’ perceived usefulness, satisfaction and attitude directly influence their intention

to continue using information systems (Liao et al., 2009).

2. Users’ perceived usefulness serves as the mediating role in the relationships between

1) users’ confirmation and their continuance intention and 2) user’s perceived

ease-of-use and their continuance intention (Liao et al., 2009).

3. Users’ satisfaction serves as the mediating role in the relationships between 1) users’

confirmation and their continuance intention and 2) users’ perceived usefulness and

their continuance intention (Liao, et al., 2009).

4. Users’ attitude serves as the mediating role in the relationships between 1) users’

perceived usefulness and their continuance intention, 2) users’ perceived ease-of-use

20

Confirmation

Perceived

Usefulness

Perceived

Ease-of-Use Attitude

IS Continuance

Intention

Satisfaction

and their continuance intention, and 3) user’s satisfaction and their continuance

intention (Liao et al., 2009; see Figure 3).

Although TCT has not been widely applied to different research, this study

considers TCT useful for suggesting the predictive roles of users’ attitude and satisfaction

on their continuance intention of information systems.

Figure 3. Technology Continuance Theory (TCT)

21

CHAPTER III

RESEARCH HYPOTHESES AND MODEL

This study employed three theoretical frameworks and relevant literature to

propose the research model. Five exogenous predictive variables (i.e., performance

expectancy, effort expectancy, social influence, facilitating conditions, and hedonic

motivation) were borrowed from UTAUT2 because of the theory’s strong predictive

power and the variables’ high relevance for the adoption of information technology.

Users’ trust and perceived risks, as two additional exogenous predictors, were developed

from social contract theory to reflect the privacy-related issues Facebook users encounter.

The present study also applied TCT and relevant literature to its model construction,

assuming that users’ attitude toward and satisfaction with Facebook are the endogenous

predictors for the proposed model. By including both exogenous and endogenous

variables, the present study provided a holistic understanding of Facebook continuance.

Attitude, Satisfaction, and Continuance Intention

TCT has been empirically verified by previous studies. Ho’s (2010) study on

e-learning continuance successfully replicated the relationships among attitude,

satisfaction, and continuance intention, as suggested by TCT. Because Facebook use is

similar to the adoption and continuing use of information technology, this study

hypothesizes that users’ attitude toward and satisfaction with Facebook positively

influence their intention to continue using Facebook. Moreover, based on the assumptions

of TCT, users’ satisfaction with Facebook also positively affects their attitude toward

Facebook. Thus, three hypotheses are proposed:

22

H1: Attitude toward Facebook positively predicts intention to continue using

Facebook.

H2: Satisfaction with Facebook positively predicts intention to continue using

Facebook.

H3: Satisfaction with Facebook positively predicts attitude toward Facebook.

Exogenous Predictors and Their Effects on Attitude and Satisfaction

Performance expectancy. The construct of performance expectancy was originally

developed from perceived usefulness, both of which measure similar psychological

concepts. Because perceived usefulness influences one’s attitude toward and satisfaction

with information systems (TCT; Ho, 2010), it is reasonable to assume that performance

expectancy would have the same effects under the context of Facebook use, which was

verified by Bonson, Escobar, and Ratkai’s (2014) study:

H4a: Performance expectancy on Facebook positively predicts attitude toward

Facebook.

H4b: Performance expectancy on Facebook positively predicts satisfaction with

Facebook.

Effort expectancy. Previous studies found that users’ perceived ease-of-use

influences their attitude (TCT model and Ho, 2010) toward and satisfaction with

Facebook use (Sibona and Choi, 2012). Because users’ perceived ease-of-use is exactly

the same as the construct of users’ effort expectancy from UTAUT2, this study

hypothesizes that users’ effort expectancy on Facebook would negatively influence their

attitude toward and satisfaction with Facebook:

23

H5a: Users’ effort expectancy on Facebook negatively predicts their attitude

toward Facebook.

H5b: User’s effort expectancy on Facebook negatively predicts their satisfaction

with Facebook.

Social influence. UTAUT2 and several behavioral and technology acceptance

models (e.g., TPB, TRA, C-TAM-TPB, TAM2 and MPCU) have provided empirical

evidence that users’ social influence affects their intention to adopt ICT. Although there is

still a lack of research examining how users’ social influence affects their attitude in a

social media or ICT context, previous studies have suggested that consumers’ subjective

norms, a construct very similar to social influence, affect their attitude toward products

and consumption behaviors (e.g., Ang, Cheng, Lim, & Tambyah, 2001; De Matos, Ituassu,

& Rossi, 2007; Phau & Teah, 2009; Tang & Farn, 2005). Thus, it is reasonable to

hypothesize that users’ social influence, such as the opinions or suggestions from their

family members and close friends, would positively influence users’ attitude toward

Facebook. Because Hsu and Chiu’s (2004) study also suggests that users’ interpersonal

influence affects their satisfaction with information systems, this study hypothesizes that

users’ social influence on Facebook use would positively influence their satisfaction with

Facebook:

H6a: User’s social influence on Facebook use positively predicts their attitude

toward Facebook.

H6b: User’s social influence on Facebook use positively predicts their satisfaction

with Facebook.

24

Facilitating conditions. A study on consumer behavior found that users’

perceiving better facilitating conditions would have more positive attitudes toward the

behavior (Limayem, Khalifa, & Chin, 2004). Yen and Gwinner’s (2003) study also

provides empirical evidence that users’ perceived behavior control directly influences

their satisfaction with information systems. Thus, this study assumes that users’

facilitating conditions on Facebook use would positively influence their attitude toward

and satisfaction with Facebook:

H7a: User’s facilitating condition on Facebook use positively predicts their

attitude toward Facebook.

H7b: User’s facilitating condition on Facebook use positively predicts their

satisfaction with Facebook.

Hedonic motivation. Hedonic motivation is considered the precursor of users’

attitude toward information systems (Childers, Carr, Peck, & Carson, 2001). A previous

study on consumer behavior also suggests both consumers’ utilitarian and hedonic values

predict their satisfaction (Ryu, Han, & Jang, 2010). Thus, this study hypothesizes that

users’ hedonic motivation for using Facebook would positively influence their attitude

toward and satisfaction with Facebook:

H8a: User’s hedonic motivation for using Facebook positively predicts their

attitude toward Facebook.

H8b: User’s hedonic motivation for using Facebook positively predicts their

satisfaction with Facebook.

Trust. Early literature found that trust predicts users’ attitude toward social

networking systems (Shin & Kim, 2010). A study on consumer behavior also suggests

25

that users’ trust in technology systems predicts their satisfaction with online

consumer-to-consumer platforms (Lu, Wang, & Hayes, 2012). Based on previous

findings, this study postulates that users’ trust in Facebook would positively predict their


H9a: User’s trust in Facebook positively predicts their attitude toward Facebook.

H9b: User’s trust in Facebook positively predicts their satisfaction with

Facebook.

Perceived Risks. De Matos and colleagues’ (2007) study on consumer behavior

found that users’ perceived risks predict their attitude toward products (De Matos et al.,

2007). Although there is a lack of literature exploring the effect of users’ perceived risks

on their satisfaction with information technology or social media, it is reasonable that

users’ perceived risks would affect their satisfaction with Facebook use. Hence, this study

hypothesizes that users’ perceived risks of Facebook use would negatively predict their


H10a: User’s perceived risks of Facebook use negatively predict their attitude

toward Facebook.

H10b: Users’ perceived risks of Facebook use negatively predict their satisfaction

with Facebook.

26

TCT


UTAUT2

H1

H2

H3

H4b

H5b

H6b

H5a

H9a

H9b

H8b

H8a

H7b

H7a

H10b

Performance Expectancy (PE)

Effort Expectancy (EE)

Facilitating Condition (FC)

Hedonic Motivation (HM)

Attitude (ATT)

Satisfaction (S)

Continued Intention (CI)

H4a

H6a Social Influence (SI)

H10a

Trust

Perceived Risks (PR)

Based on the hypotheses, the research model was proposed (see Figure 4).

Figure 4. The Research Model

27

CHAPTER IV

METHOD

Survey Research

This study intends to measure users’ perception, attitude, satisfaction, and

behavioral intention toward Facebook use and to test the relationships among these

variables by conducting a structural equation modeling (SEM) analysis. Because survey

research has been prevalently used in the field of behavioral sciences for describing the

subject population’s opinions, attitudes, behaviors etc. (Gravetter & Forzano, 2010), this

study used an online survey to collect data and later conducted a modeling test in IBM

SPSS Amos. The data were collected in late January and early February, 2014.

Sample

In order to generalize the results to the general population, the subject population

for this study is Facebook users over 18-years old in the United States (the relevant

research laws and regulations also require the participants to be 18-years old or older).

This study administrated a random sampling method by recruiting participants from

Amazon Mechanical Turk (MTurk) because of its reasonable cost compared to Qualtrics

and SurveyMonkey. Previous studies have suggested that MTurk represents a larger

population and is considered to be reliable (e.g., Buhrmester, Kwang, & Gosling, 2011).

Besides the representativeness of sampling, SEM also requires large numbers of cases.

Kenny (2012) and Kline (2011) suggest that a study of at least 200-400 cases is

considered a suitable sample size for running a SEM analysis. For this study, 450 cases

were used for the data analysis (see Table 4 for descriptive demographic statistics).

28

Table 4

Descriptive Demographic Statistics

N %

Gender

Male

Female

289

161

64.2

35.8

Age

18 to 24 years

25 to 34 years

35 to 44 years

45 to 54 years

55 to 64 years

Age 65 or older

108

227

63

36

13

3

24.0

50.4

14.0

8.0

2.9

.7

Ethnicity

White

Black or African American

Hispanic or Latino

Native American or American Indian

Asian

Other Race

333

40

26

6

42

3

74.0

8.9

5.8

1.3

9.3

.7

Education Level

Less than high school

High school or equivalent

Some college, no degree

Associate's degree

Bachelor's degree

Master's degree

Professional degree (MD, JD, etc.)

Other

4

49

140

49

169

32

6

1

.9

10.9

31.1

10.9

37.6

7.1

1.3

.2

Household Income

Less than $10,000

$10,000 - $19,999

$20,000 - $29,999

$30,000 - $39,999

$40,000 - $49,999

$50,000 - $59,999

$60,000 - $69,999

$70,000 - $79,000

$80,000 - $89,000

$90,000 - $99,000

$100,000 to $149,999

$150,000 or more

37

48

76

63

51

43

40

31

12

12

27

10

8.2

10.7

16.9

14.0

11.3

9.6

8.9

6.9

2.7

2.7

6.0

2.2

Note. The total number of cases is 450.

29

Procedure

Preparation

As a survey research involving human subjects, this study was approved by the

IRB office at The University of Southern Mississippi to ensure its less than minimum risk

(for the approval letter, see Appendix A). In order to reach participants all around the

United States, an online questionnaire consisting of 81 questions on Qualtrics

(http://qualtrics.com/) was designed for data collection (For a sample questionnaire, see

Appendix B). All the questions were either borrowed or adapted from previous scales that

have been verified to have high reliabilities. An introduction page was produced to ensure

the participants answer the questionnaire truthfully and to the best of their knowledge.

The researcher’s contact information was also provided to the participants, in case they

had any questions or concerns about this study.

Pretest

Singh (2007) suggests that a pretest is an indispensable process in a survey

research. Frequently, survey researchers only use questionnaires that have first been

pretested (Weisberg, Krosnick, & Bowen, 1996). The pretest is often conducted with the

researcher’s colleagues and target participants to detect any potential issues with or in the

instrument (Zikmund & Babin, 2009). For this study, social media professionals and

heavy users were asked to examine the questionnaire. Some minor revisions were made

based on the responses and suggestions.

Administration

This study recruited participants from Amazon Mechanical Turk (MTurk)

(https://www.mturk.com/mturk/) by paying each of them 50 cents (USD) as

30

compensation. Participants were provided a URL link to Qualtrics to answer the

questionnaire individually at their own convenience. To ensure privacy, all the

questionnaires were collected anonymously. For each part of the questionnaire, the

researcher provided detailed instructions to help participants answer individual questions

truthfully and to the best of their knowledge. The function of “force answering” for

individual questions was also used in Qualtrics to prevent participants from skipping any

questions. Only when the participants completed all of the questions, could they be

considered valid cases. Generally speaking, each participant spent about 12 to 15 minutes

finishing the survey.

Instrumentation

Screening Questions

Three screening questions (yes/no) were used for filtering the participants in this

study. The first screening question was used to ensure that the respondents were

US-based Mturk participants. The second screening question was asked to make sure all

of the participants were Facebook users. To comply with relevant research laws and

regulations, the third screening question was asked to guarantee the participants in this

study were at least 18-years old or older. Those participants answering “no” for any of the

screening questions were immediately led to a thank you page and considered invalid

cases for this study.

Exogenous Predicted Variables from UTAUT2

Five predicted variables for this study (i.e., performance expectancy, effort

expectancy, social influence, facilitating condition, and hedonic motivation) were

borrowed from UTAUT2. To measure these variables, questions using a 7-point Likert

31

scale (disagree/agree) were developed from the UTAUT2 and UTAUT models.

Performance expectancy. Four questions were asked to measure the degree to

which users believe using Facebook increases their effectiveness and efficiency in their

everyday lives: “I find Facebook useful in my daily life;” “Using Facebook increases my

chances of connecting with people that are important to me;” “Using Facebook increases

my chances of obtaining information that is important to me;” and “Using Facebook

increases my efficiency in everyday life.”

Effort expectancy. Three questions were asked to investigate the degree to which

Facebook users believe using Facebook is effortless for them: “Learning how to use

Facebook is easy for me;” “I find Facebook easy to use;” and “It is easy for me to

become skilled in using Facebook.”

Social influence. Three questions were asked to gauge the degree to which

Facebook users’ significant others believe they should use Facebook: “People who are

important to me think that I should use Facebook;” “People who influence my behavior

think that I should use Facebook;” and “People whose opinions that I value prefer that I

use Facebook.”

Facilitating conditions. Four questions were asked to examine the degree to

which Facebook users believe they have the appropriate

technologies/knowledge/resources to use Facebook: “I have the technologies necessary

(e.g., computer or smartphone) to use Facebook;” “I have the knowledge necessary to use

Facebook;” “Facebook is compatible with other technologies I use;” and “I can get help

from others when I have difficulties using Facebook.”

32

Hedonic motivation. Three questions were asked for measuring the degree of fun

or pleasure a user obtains from using Facebook: “Using Facebook is fun;” “Using

Facebook is enjoyable;” and “Using Facebook is very entertaining.”

Exogenous Predicted Variables from Social Contract Theory

Trust and perceived risks were developed from social contract theory. Because the

construct of perceived benefits is considered similar to the performance expectancy

metric from UTAUT2, trust and perceived risks were measured.

Trust. To examine the degree to which users trust in Facebook, questions using a

7-point Likert scale (disagree/agree) were directly borrowed from Fogel and Nehmad’s

(2009) study, which was originally developed by Pan and Zinkhan (2006). Four questions

were asked: “Facebook is a trustworthy social network;” “I can count on Facebook to

protect my privacy;” “I can count on Facebook to protect customers’ personal

information from unauthorized use;” and “Facebook can be relied on to keep its

promises.”

Perceived risks. Regarding the measurement for perceived risks, questions using a

7-point Likert scale (disagree/agree) were developed from Debatin, Lovejoy, Horn, and

Hughes’s (2009) study on Facebook and online privacy. Three questions were asked to

measure the degree of risks users perceive while using Facebook: “I’m worried that I may

encounter unwanted advances, stalking, or harassment on Facebook;” “I’m worried that I

may encounter damaging gossip or rumors on Facebook;” and “I’m worried that I may

encounter personal data stolen/abused by others on Facebook.”

33

Endogenous Predicted Variables from TCT

Based on TCT and previous literature, the constructs of attitude and satisfaction

were examined as the two endogenous predicted variables for the proposed model.

Attitude. Developed from Liao and colleagues’ (2009) study, four questions using

a 7-point Likert scale (disagree/agree) were asked to measure users’ general attitude

toward Facebook use: “Using Facebook would be a good idea;” “Using Facebook would

be a wise idea;” “I like the idea of using Facebook;” and “Using Facebook would be a

pleasant experience.”

Satisfaction. The questions for measuring users’ satisfaction with Facebook use

were adapted from Liao et al.’s (2009) study, which was originally developed by Spreng,

MacKenzie, and Olshavsky (1996). A statement was provided to the participants: “My

overall experience of Facebook use was ______.” Four 7-point semantic differential

scales (displeased/pleased, frustrated/contented, terrible/delighted, and

dissatisfied/satisfied) were used for measuring users’ satisfaction with Facebook use.

Dependent Variable

The dependent variable measured by this study is users’ continued intention of

Facebook use. Three questions using a 7-point Likert scale (disagree/agree) were

developed from Liao and colleagues’ (2009) study: “I intend to continue using Facebook

rather than discontinue its use;” “My intentions are to continue using Facebook rather

than using any alternative social media;” and “If I could, I would like to continue using

Facebook as much as possible.”

34

Demographic Information

Five questions concerning participants’ gender, age, ethnicity, education level, and

household income were also asked to gather basic demographic information.

35

CHAPTER V

RESULTS

This study used IBM SPSS 22 and IBM SPSS Amos 22 to conduct a structural

equation modeling (SEM) analysis. According to Dwyer, Gill, and Seetaram (2012),

“structural equation modeling (SEM) is a statistical technique for testing and estimating

causal relations using a combination of statistical data and qualitative causal

assumptions” (p.3; this definition was originally formed by Judea Pearl in 2000). Because

“SEM may be used as a more powerful alternative to multiple regression, path analysis,

factor analysis, time series analysis, and analysis of covariance” (Garson, 2008), it

provides robust results compared to other statistical methods. For this study, the

researcher followed Anderson and Gerbing’s (1988) two-step modeling approach. For the

first step of the analysis, a measurement model was created to examine the validity (i.e.

convergent validity and discriminant validity) and reliability and to conduct a model fit

analysis. For the second step, a structural model was constructed to examine the

regression paths between the variables proposed in this study.


By examining the descriptive statistics of the data, 10 major themes can be

identified: 1) The participants in general had a slightly high performance expectancy on

Facebook (M: 4.643, SD: 1.538); 2) Users slightly agreed that using Facebook is not a

tough task for them (M: 4.521, SD: 1.437); 3) Generally speaking, participants strongly

believed that their family or close friends think they should use Facebook (M: 6.132, SD:

0.912); 4) The participants’ knowledge, technology, and environment strongly supported

their Facebook use (M: 5.987, SD: 1.110); 5) Users slightly agreed that they use

36

Facebook for their hedonic motivation (M: 5.133, SD: 1.390); 6) In general, participants

slightly distrusted Facebook (M: 3.853, SD: 1.652); 7) The participants had slightly low

perceived risks when using Facebook (M: 3.964, SD: 1.697); 8) Users had a slightly

positive attitude toward Facebook (M: 4.881, SD: 1.404); 9) Users were slightly satisfied

with Facebook (M: 5.006, SD: 1.453); and 10) Generally speaking, participants slightly

agreed that they will continue using Facebook (M: 4.906, SD: 1.537; see Table 5).

Table 5


Construct Item Question M SD

Performance

Expectancy (PE) PE1 I find Facebook useful in my daily life. 4.653 1.551

PE2 Using Facebook increases my chances of connecting

people that are important to me. 5.142 1.460

PE3 Using Facebook increases my chances of obtaining

information that are important to me. 5.022 1.427

PE4 Using Facebook increases my efficiency in everyday life 3.753 1.714

4.643 1.538

Effort

Expectancy (EE) EE1 Learning how to use Facebook is easy for me. 4.518 1.450

EE2 I find Facebook easy to use. 4.391 1.490

EE3 It is easy for me to become skillful at using Facebook. 4.653 1.371

4.521 1.437

Social Influence

(SI)

SI1

People who are important to me think that I should use

Facebook. 6.144 0.915

SI2

People who influence my behavior think that I should use

Facebook. 6.142 0.923

SI3 People whose opinions that I value prefer I use

Facebook. 6.111 0.898

6.132 0.912

Facilitating

Condition (FC)

FC1

I have the technologies necessary (e.g. computer or

smartphone) to use Facebook. 6.244 1.077

FC2 I have the knowledge necessary to use Facebook. 6.233 0.986

FC3 Facebook is compatible with other technologies I use. 6.064 1.046

FC4 I can get help from others when I have difficulties using

Facebook. 5.407 1.329

5.987 1.110

37


Construct Item Question M SD

Hedonic

Motivation (HM) HM1 Using Facebook is fun. 5.127 1.351

HM2 Using Facebook is enjoyable. 5.189 1.347

HM3 Using Facebook is very entertaining. 5.082 1.471

5.133 1.390

Trust Trust1 Facebook is a trustworthy social network. 4.262 1.591

Trust2 I can count on Facebook to protect my privacy. 3.660 1.690

Trust3

I can count on Facebook to protect customers’ personal

information from unauthorized use. 3.724 1.683

Trust4 Facebook can be relied on to keep its promises. 3.764 1.644

3.853 1.652

Perceived Risks

(PR)

PR1

I’m worried that I may encounter unwanted advances,

stalking, or harassment on Facebook. 3.649 1.716

PR2 I’m worried that I may encounter damaging gossip or

rumors on Facebook. 3.751 1.704

PR3 I’m worried that I may encounter personal data

stolen/abused by others on Facebook. 4.438 1.670

3.964 1.697

Attitude (ATT) ATT1 Using Facebook would be a good idea. 4.936 1.351

ATT2 Using Facebook would be a wise idea. 4.540 1.395

ATT3 I like the idea of using Facebook. 4.998 1.458

ATT4 Using Facebook would be a pleasant experience. 5.049 1.410

4.881 1.404

Satisfaction (S) S1

My overall experience of Facebook use was

displeased/pleased. 5.142 1.427

S2 My overall experience of Facebook use was

frustrated/contented. 4.980 1.501


terrible/delighted. 4.882 1.364


dissatisfied/satisfied. 5.020 1.518

5.006 1.453

Continuance

Intention (CI)

CI1

I intend to continue using Facebook rather than

discontinue its use. 5.280 1.396

CI2

My intentions are to continue using Facebook than use

any alternative social media. 4.933 1.548

CI3 If I could, I would like to continue using Facebook as

much as possible. 4.504 1.668

4.906 1.537

38

Validity and Reliability Measures

Convergent Validity

According to Hair, Black, Babin, Anderson, and Tatham’s (2006) definition,

convergent validity is “the extent to which indicators of a specific construct converge or

share a high proportion of variance in common” (p. 771). After cleaning the data in IBM

SPSS 22, the researcher created a measurement model in IBM SPSS Amos 22 (see Figure

5). Followed by Hair et al. (2006), Kline (2011), and Schumacker, and Lomax’s (2004)

suggestions, the researcher examined the factor loading for each measurement item and

calculated the average variance extracted (AVE) and composite reliability (CR) values to

ensure the convergent validity for the measurement model. As can be seen from Table 6,

the factor loadings for all the measurement items ranged from 0.715 to 0.965 (after

deleting those items below .70), which are all greater than the threshold value of .70. The

AVE values for the constructs also meet the threshold requirement (> .50), ranging

from .764 to .982. For CR values, all the constructs are above the minimum threshold

of .60. The examination of factor loading, AVE, and CR values indicates the convergent

validity was met by the proposed model (for details, see Table 6).

39

Figure 5. Measurement Model in IBM SPSS Amos 22

Table 6

Convergent Validity

Construct/Factor Item Standardized

Loading

Average variance

extracted (AVE)

Composite

reliability (CR)

Reliability

(Alpha Value)

Performance

Expectancy (PE)

PE1

PE2

.874

.715

0.885 0.939 .768

40


Construct/Factor Item Standardized

Loading

Average variance

extracted (AVE)

Composite

reliability (CR)

Reliability

(Alpha Value)

Effort Expectancy

(EE)

EE1

EE2

EE3

.910

.926

.830

0.947 0.982 .918

Social Influence

(SI)

SI1

SI2

SI3

.908

.938

.870

0.982 0.990 .932

Facilitating

Condition (FC)

FC1

FC2

FC3

.828

.964

.762

0.959 0.986 .886

Hedonic

Motivation (HM)

HM1

HM2

HM3

.955

.965

.879

0.975 0.991 .840

Trust Trust1

Trust2

Trust3

Trust4

.829

.964

.972

.925

0.964 0.991 .958

Perceived Risks

(PR)

PR1

PR2

.841

.888

0.764 0.866 .855

Attitude (ATT) ATT1

ATT2

ATT3

.805

.934

.943

0.962 0.987 .895

Satisfaction (S) S1

S2

S3

.908

.938

.870

0.948 0.982 .896

Continuance

Intention (CI)

CI1

CI2

CI3

.806

.807

.875

0.914 0.970 .870

Discriminant Validity

According to Hair et al.’s (2006) definition, discriminant validity is “the extent to

which a construct is truly distinct from other construct” (p. 771). Followed by Hair and

colleagues (2006), Kline (2011), and Schumacker, and Lomax’s (2004) suggestion,

discriminant validity was tested by comparing the square root of the AVE of a specific

construct (the bold numbers in Table 7) to the factor correlation coefficients between the

specific construct and other constructs. The results show the square root of the AVE for

each construct is greater than its correlation coefficients with other constructs, indicating

a good discriminant validity for the proposed model.

41

Table 7

Discriminant Validity

Construct PE EE SI FC HM Trust PR ATT S CI

PE

EE

SI

FC

HM

Trust

PR

ATT

S

CI

0.940

0.515

0.258

0.212

0.700

0.477

-0.049

0.735

0.704

0.865

0.973

0.066

0.007

0.425

0.344

0.009

0.428

0.374

0.393

0.991

0.444

0.312

0.147

-0.076

0.299

0.293

0.246

0.979

0.126

-0.009

-0.149

0.108

0.193

0.185

0.987

0.496

-0.028

0.941

0.809

0.717

0.982

-0.238

0.550

0.651

0.537

0.874

-0.113

-0.189

-0.138

0.981

0.845

0.757

0.974

0.767

0.956

Reliability

According to Hair and colleagues’ (2006) definition, reliability is “an assessment

of the degree of consistency between multiple measurements of a variable” (p. 137). This

study used Cronbach’s alpha to examine reliability because it is considered the most

common measurement among many other reliability statistics. The Cronbach’s alpha

reliability for the constructs of PE, EE, SI, FC, HM, Trust, PR, ATT, S, and CI were

calculated using IBM SPSS 22. The results showed the coefficients for Cronbach’s alpha

range from .84 to .96 for the constructs of EE, SI, FC, HM, Trust, PR, ATT, S, and CI,

indicating a high internal consistency. The construct of PE had an acceptable internal

consistency with .77 for Cronbach’s alpha. Overall, the measurement items for individual

constructs are considered reliable based on the threshold value of .70 suggested by

Hatcher and Stepanski (1994; for details, see Table 6).

Examination of Model Fit

According to El-Hajjar (2014), “the goodness-of-fit of a statistical model

describes how well it fits a set of observations” (p. 233). Researchers usually compare the

observed values of a set of indices to the recommended values to determine the

42

goodness-of-fit of a proposed model (Hair et al., 2006; Kline, 2011; Schumacker &

Lomax, 2004). To examine the measurement model fit, a list of goodness-of-fit indices

were used for comparison based on the suggestions by Hair et al. (2006), Kline (2011),

and Schumacker and Lomax (2004). These indices include three major types: 1) absolute

fit indices: chi-squared/df (the ratio between the chi-square and degrees of freedom), GFI

(Goodness of Fit Index), AGFI (Adjusted Goodness of Fit Index), and RMSEA (Root

Mean Square Error of Approximation); 2) incremental fit indices: NFI (Normed Fit

Index), RFI (Relative Fit Index), IFI (Incremental Fit Index), TLI (Tucker-Lewis Index),

CFI (Comparative Fit Index); and 3) parsimonious fit indices: PCFI (Parsimony

Comparative Fit Index), PNFI (Parsimonious Normed-Fit Index). The results indicate that

the proposed model has a good fit to the collected data when rounding the actual values

of indices to one decimal place. Table 8 shows the recommended value and actual value

for individual indices.

Table 8

Fit Indices for the Measurement Model

Goodness-of-fit indices Recommended value Actual value

Absolute fit indices χ2/df <3 2.263

GFI >0.90 0.893

AGFI >0.80 0.860

RMSEA <0.08 0.053

Incremental fit indices NFI >0.90 0.943

RFI >0.90 0.930

IFI >0.90 0.967

TLI >0.90 0.960

CFI >0.90 0.967

Parsimonious fit indices PCFI >0.50 0.791

PNFI >0.50 0.777

43

Hypotheses Testing

For the second step of the data analysis, the researcher drew all the paths

hypothesized by this study to construct a structural model in IBM SPSS Amos 22 (see

Figure 6). As can be seen from Figure 7, 12 out of 17 paths were supported at a

significance level of .05. One path was marginally supported at an alpha value of .10.

Attitude is influenced by satisfaction (β = .158), performance expectancy (β = .137),

facilitating conditions (β = -.055), hedonic motivation (β = .713), and perceived risks (β =

-.060). Satisfaction is affected by performance expectancy (β = .238), effort expectancy

(β = .075, p = .068), facilitating conditions (β = .075), hedonic motivation (β = .515),

trust (β = .289), and perceived risks (β = -.086). Continuance intention is predicted by

attitude (β = .374) and satisfaction (β = .464). The R2 value shows that the model explains

92% of the variance in users’ attitude toward Facebook, 79% of the variance in users’

satisfaction with Facebook, and 65% of the variance in users’ intention to continue using

Facebook. Table 9 shows a summary table of the hypotheses testing in this study.

44

Figure 6. Structural Model in IBM SPSS Amos 22

45

0.238***

-0.006

-0.007

0.015

0.289***

0.515***

0.713***

0.075*

-0.055*

-0.086**

Performance Expectancy (PE)

Effort Expectancy (EE)

Facilitating Condition (FC)

Hedonic Motivation (HM)

Attitude (ATT) (R2= 0.918)

Satisfaction (S) (R2= 0.786)

Continuance Intention (CI)

(R2= 0.649)

.0.137**

0.012 Social Influence (SI)

-0.060*

Trust

Perceived Risks (PR)

0.158**

0.374***

0.464***

-0.063＋

*p<0.05; **p<0.01; ***p<0.001. ＋

p<0.10

Figure 7. Structural Model

Table 9

Hypotheses Testing

Hypothesis Path β t p Result

H1 ATT CI 0.374 4.990 *** Supported

H2 S CI 0.464 5.993 *** Supported

H3 S ATT 0.158 3.075 0.002 Supported

H4a PE ATT 0.137 3.175 0.001 Supported

H4b PE S 0.238 4.259 *** Supported

H5a EE ATT -0.007 -0.268 0.789 Not Supported

H5b EE S -0.063 -1.822 0.068 Marginally Supported

H6a SI ATT 0.012 0.521 0.602 Not Supported

H6b SI S -0.006 -0.186 0.853 Not Supported

H7a FC Att -0.055 -2.280 0.023 Supported

H7b FC S 0.075 2.291 0.022 Supported

H8a HM ATT 0.713 15.539 *** Supported

H8b HM S 0.515 10.146 *** Supported

H9a Trust ATT 0.015 0.520 0.603 Not Supported

H9b Trust S 0.289 7.975 *** Supported

H10a PR ATT -0.060 -2.588 0.010 Supported

46


Hypothesis Path β t p Result

H10b PR S -0.086 -2.761 0.006 Supported

Mediation Analysis

Following the suggestion of Baron and Kenny (1986), this study used the

technique of bootstrap in SPSS AMOS 22 to examine the direct, indirect, and total effects

on users’ continuance intention from various predictors in the research model. Table 10

shows the bootstrap values based on a sample size of 2000 and with 95% bias-corrected

confidence intervals. The results of the total effects suggest that attitude (β = .374),

satisfaction (β = .523), performance expectancy (β = .176), hedonic motivation (β = .536),

trust (β = .157), and perceived risks (β = -.067) are all determinant factors for Facebook

use continuance. These total effects include two direct effects from attitude (β = .374) and

satisfaction (β = .464). Regarding the indirect effects, satisfaction (β = .157), performance

expectance (β = .176), hedonic motivation (β = .536), trust (β = .157), and perceived risks

(β = -.076) are all considered significant predictors for continuance intention of Facebook

use.

Table 10

Bootstrap Values for Direct, Indirect, and Total Effect (Continuance Intention)

Varia

ble

Continuance Intention

Direct Effect Indirect effect Total Effect R2

β p 95% C.I. β p 95% C.I. β p 95% C.I.

ATT .374 .001 (.147, .609) - - - .374 .001 (.147, .609) 0.649

S .464 .001 (.328, .710) .059 .041 (.002, .176) .523 .001 (.328, .710)

PE - - - .176 .002 (.087, .294) .176 .002 (.087, .294)

EE - - - -.035 .146 (-.085, .012) -.035 .146 (-.085, .012)

SI - - - .002 .879 (-.037, .048) .002 .879 (-.037, .048)

FC - - - .019 .332 (-.022, .072) .019 .332 (-.022, .072)

47


Variable Continuance Intention

Direct Effect Indirect effect Total Effect R2

β p 95% C.I. β p 95% C.I. β p 95% C.I.

HM - - - .536 .000 (.420, .640) .536 .000 (.420, .640) 0.649

Trust - - - .157 .001 (.092, .224) .157 .001 (.092, .224)

PR - - - -.067 .003 (-.115, -.023) -.067 .003 (-.115, -.023)

48

CHAPTER VI

DISCUSSION

Discussion of Results

This theory-driven study establishes a new continuance model for Facebook use,

explaining 65% of the variance for Facebook users’ continuance intention. The results

suggest the following five major findings:

1. Users’ attitude toward Facebook (β = .374, p < .001) and satisfaction with Facebook

use (β = .464, p < .001) are the two strongest predictors for their continuance

intention.

2. Users’ satisfaction with Facebook use affects their attitude toward Facebook (β = .158,

p < .05), which indirectly influences their continuance intention of Facebook use (β

= .059, p < .05, 95% C.I. = .002, .176).

3. Users’ performance expectancy, hedonic motivation, and perceived risks directly

influence their attitude toward Facebook (performance expectancy: β = .137, p < .01;

hedonic motivation: β = .713, p < .001; perceived risks: β = -.060, p < .05) and

satisfaction with Facebook use (performance expectancy: β = .238, p < .01; hedonic

motivation: β = .515, p < .001; perceived risks: β = -.086, p < .01), which later affect

users’ Facebook continuance intention (indirect effect from performance expectancy:

β = .176, p < .01, 95% C.I. = .087, .294; indirect effect from hedonic motivation: β

= .536, p < .001, 95% C.I. = .420, .640; and indirect effect from perceived risks: β =

-.067, p < .01, 95% C.I. = -.115, -.023).

49

4. Users’ trust in Facebook affects their satisfaction with Facebook use (β = .289, p

< .001), which later influences their continuance intention of Facebook use (indirect

effect from users’ trust: β = -.067, p < .01, 95% C.I. = .092, .224).

5. Although users’ facilitating condition does not influence their continuance intention,

it does predict users’ attitude toward Facebook (β = -.055, p < .05) and satisfaction

with Facebook use (β = .075, p < .005).

These results suggest that the theories applied in this study (i.e., UTAUT2, social

contract theory, and TCT) can be used to explain the continuance intention of Facebook

use, with only minor adjustments required to tailor the theories to the context of social

media research. Both theoretical ramifications and marketing implications are provided

and discussed in the rest of this chapter.

Theoretical Ramifications

The findings from this study are consistent with the TCT model, and a previous

study (Ho, 2010) in information system use continuance. For example, the total effects

for the structural model suggest that users’ attitude and satisfaction with Facebook are the

two strongest precursors for Facebook use continuance intention. The mediation analysis

also reveals both the direct and indirect effects from users’ satisfaction on continuance

intention. Regarding the indirect effect, users’ satisfaction with Facebook influences their

attitude toward Facebook, which later predicts users’ continuance intention of Facebook

use. Although the proposed research model in this study did not comprehensively test all

the assumptions by TCT, this study suggests the potential for the TCT model to be

extended to the context of social media research.

50

This study suggests UTAUT2 can help better understand users’ continued

intention of Facebook use. Because of social media’s unique characteristics compared to

other technology, a couple of constructs in UTAUT2 are not as strong predictors for

continued use as other literature suggests. Performance expectancy and hedonic

motivations from UTAUT2 are considered two determent predictors for continuance

intention of Facebook, mediated through users’ attitude toward Facebook and satisfaction

with Facebook use. This means that users continue using Facebook if they consider

Facebook useful or fun either for their everyday lives or jobs. However, when using

users’ attitude and satisfaction with Facebook as two mediating factors, this study did not

find any statistically significant influences from effort expectancy, social influence, and

facilitating conditions. It is possible that the prevalence of wireless internet and mobile

devices as well as the popularity of Facebook have decreased the influences from users’

effort expectancy and facilitating conditions. Since users’ mobile technology and prior

use experience has made Facebook use an effortless activity, users’ effort expectancy and

facilitating conditions may no longer predict their intention to continue using Facebook.

Users’ social influence, on the other hand, may be a strong predictor for users’ initial

adoption behavior of information technology (e.g., Venkatech et al., 2003; Venkatesh et

al., 2012) rather than continuance intention. Even so, users’ facilitating condition, based

on the measurement model, influences their attitude toward Facebook and satisfaction

with Facebook use; however, it is users’ attitude and satisfaction, rather than their

facilitation condition, that predict their continuance intention of Facebook use.

The results of this study also suggest that users’ continuance of Facebook is an

implicit social contract. When people use Facebook, they believe that Facebook is a

51

reliable social networking company and trust that Facebook would not use their personal

information for unauthorized purposes. Nevertheless, if Facebook breaks this social

contract with its users, people will have lower degrees of trust in this online social

networking platform. Facebook users’ levels of perceived risks will also likely soar

because they consider Facebook to be untrustworthy. In this respect, users’ trust and

perceived risks affect their attitude toward Facebook as well as their satisfaction with

Facebook use, which indirectly determine their continuance intention of Facebook. These

findings echo the results from previous literature that users’ perceived risks affect their

willingness to disclose personal information on Facebook (Krasnova et al., 2010). Users’

privacy concern on Facebook, therefore, has become one of the significant reasons for

users to discontinue their Facebook use (Stieger et al., 2013).

Practical and Marketing Implications

Based on the empirical findings in this study, several marketing and practical

suggestions are also provided to improve the business practices of social media

companies and marketers.

For social media companies and marketers, this study suggests that boosting

users’ positive attitude toward and satisfaction with Facebook would ensure their use

continuance intention. To ensure users’ positive attitude, social media companies are

suggested to enhance users’ satisfaction, performance expectancy, facilitating conditions,

and hedonic motivation. At the same time, users’ perceived risks need to be alleviated. On

the other hand, users’ satisfaction with Facebook use can be boosted by ensuring their

performance expectancy, effort expectancy, facilitating conditions, hedonic motivation,

and trust are met. Similarly, social media companies need to decrease users’ perceived

52

risks to ensure their satisfaction with Facebook. Other than enhancing users’ positive

attitude toward Facebook and satisfaction with Facebook use, increasing users’

performance expectancy, hedonic motivation, and trust, as well as decreasing their

perceived risks, would ensure users’ continuance intention, mediated through their

attitude toward and satisfaction with Facebook.

Based on the effect of performance expectancy, users are more likely to continue

using Facebook when they consider Facebook to be useful in their everyday lives or jobs.

This study therefore encourages social media companies to develop useful functions for

their users. For example, the birthday reminder is considered practical for most Facebook

users, as Facebook users will now not forget to send birthday wishes to their friends.

Based on the effect of the facilitating condition found by this study, social media

companies need to enhance their platform compatibility with different technological

platforms (e.g., Mac OX, PC Windows, Linex, etc.) or mobile devices (e.g., iPhone, iPad,

Android Phone, etc.) to increase users’ level of perceived facilitating conditions. Since

some users may still use old computer systems and out-dated browsers, or they do not

have the most up-to-date Java or Flash to appropriately display multi-media social media

platforms, social media companies may consider providing different versions of the sites

based on users’ system and browser capability. By doing so, more users will be able to

access the social media platforms even though they only have very basic technological

facilities. Based on the effect of hedonic motivation, social media companies are

encouraged to increase users’ fun and enjoyable experience while using social media. For

example, game applications have been widely applied by Facebook, which might explain

part of the reasons for why Facebook has been rated the most popular social networking

53

website. This study suggests that other social media platforms adopt a similar strategy to

provide a more fun and enjoyable experience for their users, which will enhance their use

continuance intention.

Last, this study suggests that the business integrity of social media companies and

users’ risk concerns play an important role in users’ continuance intention of social media.

Based on the effect of trust found in this study, social media companies may consider

enhancing users’ trust beliefs for using their platforms. To achieve this goal, up-to-date

terms and policies regarding how social media companies can utilize users’ personal

information for customized service are required. Social media companies also need to

keep their promises to behave properly and to protect users’ personal information from

unauthorized use. On the other hand, based on the effect of perceived risks, social media

companies should enhance their privacy protections to alleviate users’ risk concerns. In

addition to the use of an up-to-date encryption algorithm for the security of online

platforms, social media companies are strongly encouraged to perform data

anonymization before applying users’ personal information for targeted advertising and

marketing. When sharing information with third parties for services, no personally

identifiable information should be included or provided.

54

CHAPTER VII

LIMITATIONS AND FUTURE RESEARCH

Although this study creates a strong and holistic model for predicting Facebook

use continuance, the research has its limitations. Future research projects are encouraged

based on the following suggestions.

First, this study borrows constructs and develops its measure scales from other

research in the fields of ICT and information science. Because social media use may be

different from the adoption of communication technology, this discrepancy might cause

doubt about the validity of this study regarding what has been measured. Future studies

should conduct exploratory factor analyses to develop original scales as well as to modify

relevant psychological constructs based on the context of social media use. For example,

the construct of “effort expectancy” or “perceived effort” has been widely applied to ICT

adoption studies. When it comes to Facebook, already having a high penetration rate in

the United States, no one really needs to take the effort to use it. Nevertheless, to use

Facebook and to be proficient in using it might be different for different users. Some

people use Facebook without the ability to manage their content and privacy settings,

which may be a potential factor affecting their willingness to continue using Facebook.

Second, this study uses the two endogenous variables of attitude and satisfaction

to provide a holistic understanding of Facebook use continuance. However, the indirect

effects from various exogenous variables are not distinguished regarding whether they are

mediated through attitude or satisfaction. For example, the results of this study suggest

that perceived risks affect use continuance intention, mediated through users’ attitude

toward and satisfaction with Facebook. More studies are required to explore whether this

55

mediation effect is mainly from attitude or satisfaction, or maybe both. The influence of

attitude on satisfaction also needs further exploration. Because the difference between

users’ attitude and satisfaction may affect the inferred implications of the structural model,

future research is needed to examine the mediating roles of attitude and satisfaction more

carefully.

Third, although Facebook is one of the most popular social networking sites, it

might not be a good representative for other social media platforms, such as Twitter,

Instagram, Google Plus, etc. As more and more alternative social networking sites

become more popular and reach large numbers of users, follow-up research projects may

apply a similar research model for examining other social media platforms as well as

comparing the similar and contradictory results among different social media outlets.

This type of cross-platform research will enrich our understanding of social media use

continuance both at the theoretical and practical levels. As modern advertisers and

marketers target their customers through multiple social media platforms simultaneously,

it is essential for researchers to examine multiple social networking sites as a whole

rather than focus on one single social media company.

Fourth, the nature of social media is globalized and geographically boundless. As

advertisers and marketers utilize social media to target customers all around the world, it

is imperative for researchers to continue exploring overseas users from diverse cultural

backgrounds or geographical areas. To better understand universal users’ social media use

continuance, studies must take cultural factors, which have not been fully explored by the

existing social media literature and the present study, into consideration.

56

APPENDIX A

IRB APPROVAL LETTER

57

APPENDIX B

SAMPLE QUESTIONNAIRE

ATTENTION!!! DUPLICATE RESPONDERS WILL NOT BE PAID! THE

RESEARCHER WILL ALSO REJECT THE ASSIGNMENTS SUBMITTED BY

DUPLICATE RESPONDERS!

I understand that I must be a US-BASED WORKER to have my assignment approved

and get paid from MTurk.

I am a US-based worker.

I decide to leave the survey.

I understand that I must be a FACEBOOK USER to answer this questionnaire. If I am not

a FACEBOOK USER, I will not have my assignment approved or get paid from MTurk.

I am a Facebook user.

I decide to leave the survey.

I am 18 or older and agree to participate in this study.

Yes

No

58

This question asks your experience with the use of Facebook. Please choose if you agree

or disagree with the following statements.

Strongly Disagree

Disagree Somewhat Disagree

Neither Agree nor Disagree

Somewhat Agree

Agree Strongly Agree

Learning how to

use Facebook is

easy for me.

I find Facebook

easy to use.

It is easy for me to

become skillful at

using Facebook.

People who are

important to me

think that I should

use Facebook.

People who

influence my

behavior think that

I should use

Facebook.

People whose

opinions that I

value prefer I use

Facebook.

59

This question asks your opinions about Facebook use. Please choose if you agree or

disagree with the following statements.

Strongly Disagree



Somewhat Agree


Using Facebook is

fun.

Using Facebook is

enjoyable.

Using Facebook is

very entertaining.

Using Facebook

would be a good

idea.

Using Facebook

would be a wise

idea.

I like the idea of

using Facebook.

Using Facebook

would be a

pleasant

experience.

60

Please choose the bubble that best represents how you feel about each statement.

Strongly Disagree



Somewhat Agree


I use Facebook to

post a message on

my friend’s wall.

I use Facebook to

occupy my time.

I use Facebook to

find more interesting

people than in real

life.

I use Facebook to

read other people’s

profiles.

I use Facebook to

have fun.

I use Facebook when

no one to talk or be

with.

I use Facebook to

organize or join

events.

I use Facebook to

share / post

photographs.

I use Facebook to use

applications.

I use Facebook to

meet new people.

I use Facebook to

view other people’s

friends.

I use Facebook to see

what people have put

as their status.

61

This question asks your Facebook usage. Please choose if you agree or disagree with the

following statements.

Strongly Disagree



Somewhat Agree


The use of Facebook

has become a habit

for me.

I am addicted to

using Facebook.

I must use Facebook.

Using Facebook has

become natural to

me.

I have the

technologies

necessary (e.g.

computer or

smartphone) to use

Facebook.

I have the knowledge

necessary to use

Facebook.

Facebook is

compatible with

other technologies I

use.

I can get help from

others when I have

difficulties using

Facebook.

62

This question asks your perceptions of Facebook use. Please choose if you agree or

disagree with the following statements.

Strongly Disagree


Neither Agree

nor Disagree

Somewhat Agree


I intend to continue

using Facebook rather

than discontinue its use.

My intentions are to

continue using

Facebook than use any

alternative social

media.

If I could, I would like

to continue using

Facebook as much as

possible.

I find Facebook useful

in my daily life.

Using Facebook

increases my chances

of connecting people

that are important to

me.

Using Facebook

increases my chances

of obtaining

information that are

important to me.

Using Facebook

increases my efficiency

in everyday life.

63

Please choose the bubble that best represents how you feel about each statement.

Strongly Disagree



Somewhat Agree


I use Facebook to

send a message to a

friend.

I use Facebook to

pass time when I feel

bored.

I use Facebook to

develop romantic

relationship.

I use Facebook to see

other people’s

pictures.

I use Facebook

because it is cool.

I use Facebook to

feel less lonely.

I use Facebook to

join groups.

I use Facebook to tag

photos.

I use Facebook to

play games.

I use Facebook for

advanced search to

look for specific

types of people.

I use Facebook to

look at the profiles of

people I don’t know.

I use Facebook to

update my own

status.

64

This question asks your beliefs about Facebook. Please choose if you agree or disagree

with the following statements.

Strongly Disagree



Somewhat Agree


Facebook is a

trustworthy social

network.

I can count on

Facebook to protect my

privacy.

I can count on

Facebook to protect

customers’ personal

information from

unauthorized use.

Facebook can be relied

on to keep its promises.

My overall experience of Facebook use was:

1 2 3 4 5 6 7

Displeased Pleased

Contented Frustrated

Terrible Delighted

Satisfied Dissatisfied

Please rate your overall satisfaction with Facebook (1 star = low satisfaction; 4 stars =

fair; 7 stars = high satisfaction).

My overall satisfaction with Facebook

☆ ☆ ☆ ☆ ☆ ☆ ☆

65

This question asks your opinions about privacy issues on Facebook. Please choose if you

agree or disagree with the following statements.

Strongly Disagree


Neither Agree

nor Disagree

Somewhat Agree


I am concerned that the

information I submit on

Facebook could be

misused.

I am concerned that a

person can find private

information about me

on the Internet.

I am concerned about

submitting information

on Facebook, because

of what others might do

with it.

I am concerned about

submitting information

on Facebook, because it

could be used in a way

I did not foresee.

I’m worried that I may

encounter unwanted

advances, stalking, or

harassment on

Facebook.


encounter damaging

gossip or rumors on

Facebook.


encounter personal data

stolen/abused by others

on Facebook.

66

How many YEARS have you used Facebook?

_________ year(s)

How many DAYS do you use Facebook on a weekly basis?

1 day

2 days

3 days

4 days

5 days

6 days

7 days

How many TIMES do you use Facebook on a daily basis?

_________ time(s)

How many MINUTES do you usually spend on Facebook on a daily basis?

_________ minute(s)

How often do you update your status on Facebook?

Never

Rarely

Sometimes

Often

All of the Time

67

How many friends do you have on Facebook? (You may have to check your Facebook

account to answer this question.)

_________

What country do you currently live in?

_________

What is your gender?

Male

Female

What is your age?

Under 18 years

18 to 24 years

25 to 34 years

35 to 44 years

45 to 54 years

55 to 64 years

Age 65 or older

What Is your ethnicity?

White

Black or African American

Hispanic or Latino

Native American or American Indian

Asian

Native Hawaiian and Other Pacific Islander

Other race

68

What is your education level?

Less than high school

High school or equivalent

Some college, no degree

Associate's degree

Bachelor's degree

Master's degree

Doctoral degree

Professional degree (MD, JD, etc.)

Other

What is your occupation?

_________

What was your total household income last year from all sources?

Less than $10,000

$10,000 - $19,999

$20,000 - $29,999

$30,000 - $39,999

$40,000 - $49,999

$50,000 - $59,999

$60,000 - $69,999

$70,000 - $79,000

$80,000 - $89,000

$90,000 - $99,000

$100,000 to $149,999

$150,000 or more

69

REFERENCES

Ajzen, I. (2002). Residual effects of past on later behavior: Habituation and reasoned

action perspectives. Personality & Social Psychology Review, 6(2), 107-122.

Ajzen, I., & Fishbein, M. (2005). The influence of attitudes on behavior. In D. Albarracín,

B. T. Johnson & M. P. Zanna (Eds.), The handbook of attitudes (pp. 173-221).

Mahwah, NJ: Erlbaum.

Alawan, A. A., Dwivedi, Y. K., & Williams, M. D. (2013). Adoption of self-service

technology by Jordanian customers. Proceedings of Swansea University Business

School Postgraduate Research Conference (pp.8-16). Swansea, Wales, United

Kingdom.

Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A

review and recommended two-step approach. Psychological Bulletin, 103(3),

411-423.

Ang, S. H., Cheng, P. S., Lim, A. C., & Tambyah, S. K. (2001). Spot the difference:

Consumer responses towards counterfeits. Journal of Consumer Marketing, 18(3),

219-235.

Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in

social psychological research: Conceptual, strategic, and statistical considerations.

Journal of Personality and Social Psychology, 51(6), 1173.

Bolton, R. N., & Drew, J. H. (1991). A longitudinal analysis of the impact of service

changes on customer attitudes. Journal of Marketing, 55(1), 1-9.

70

Bonsón, E., Escobar, T., & Ratkai, M. (2014). Testing the inter-relations of factors that

may support continued use intention: The case of Facebook. Social Science

Information, 53(3), 293-310.

Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in the household:

A baseline model test and extension incorporating household life cycle. MIS

Quarterly, 29(3), 399-426.

Buhrmester, M., Kwang, T., & Gosling, S. (2011). Amazon’s Mechanical Turk A New

Source of Inexpensive, Yet High-Quality, Data? Perspectives on Psychological

Science, 6(1), 3-5.

Chan, K. Y., Gong, M., Xu, Y., & Thong, J. Y. L. (2008). Examining user acceptance of

SMS: An empirical study in China and Hong Kong. PACIS 2008 Proceedings

(paper 294). Suzhou, China.

Chang, C.-W., & Heo, J. (2014).Visiting theories that predict college students’

self-disclosure on Facebook. Computers in Human Behavior, 30(1), 79-86.

Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian

motivations for online retail shopping behavior. Journal of Retailing, 77(4),

511-535.

Chong, A. Y.-L., & Ngai, E. T. W. (2013). What influences travelers’ adoption of a

location-based social media service for their travel planning? PACIS 2013

Proceedings (paper 210). Jeju Island, Korea.

Churchill, G. A., & Suprenant, C. (1982). An investigation into the determinants of

customer satisfaction. Journal of Marketing Research, 19(4), 491-504.

71

Culnan, M. J., & Armstrong, P. K. (1999). Information privacy concerns, procedural

fairness, and impersonal trust: An empirical investigation. Organization Science,

10(1), 104-115.

Cutler, K. (2013). Facebook’s Q4 revenue rises 40% to $1.59B, shares down slightly in

after-hours. Retrieved from

http://techcrunch.com/2013/01/30/facebooks-q4-revenue-rises-40-to-1-59b-shares-

decline-7-percent-in-after-hours/

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer

technology: A comparison of two theoretical models. Management Science, 35(8),

982-1002.

De Matos, C. A., Ituassu, C. T., & Rossi, C. A. V. (2007). Consumer attitudes toward

counterfeits: A review and extension. Journal of Consumer Marketing, 24(1),

36-47.

Debatin, B., Lovejoy, J. P., Horn, A. K., & Hughes, B. N. (2009). Facebook and online

privacy: Attitudes, behaviors, and unintended consequences. Journal of

Computer-Mediated Communication, 15(1), 83-108.

Dodds, W. B., Monroe, K. B., & Grewal, D. (1991). Effects of price, brand, and store

information on buyers. Journal of Marketing Research, 28(3), 307-319.

Dwyer , L., Gill, A., & Seetaram, N., (eds.) (2012). Handbook of research methods in

tourism: Quantitative and qualitative approaches. Cheltenham, UK: Edward Elgar.

El-Hajjar, S. T. (2014). Basic & business course in statistics II. Bloomington, IN:

AuthorHouse.

72

Facebook (2013) Facebook reports first quarter 2013 results. Retrieved from

http://investor.fb.com/releasedetail.cfm?ReleaseID=761090

Facebook terms and policies (2013). Retrieved from https://www.facebook.com/policies/

Fogel, J., & Nehmad, E. (2009). Internet social network communities: Risk taking, trust,

and privacy concerns. Computers in Human Behavior, 25(1), 153-160.

Gallant, L. M., & Boone, G. M. (2011). Communicative informatics: An active and

creative audience framework of social media. Cognition, Communication,

Co-Operation, 9(2), 231-246.

Garson, D. (2008). Statnotes: Topics in multivariate analysis. Retrieved from

http://faculty.chass.ncsu.edu/garson/pa765/statnote.htm

Gravetter, F. J., & Forzano, L. B. (2010). Research methods for the behavioral sciences.

Belmont, CA: Wadsworth.

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006).

Multivariate data analysis (6th edition). Upper Saddle River, NJ: Pearson.

Hargittai, E. (2007). Whose space? Differences among users and non-users of social

network sites. Journal of Computer-Mediated Communication, 13(1), 276-297.

Hargittai, E., & Litt, E. (2011). The tweet smell of celebrity success: Explaining variation

in Twitter adoption among a diverse group of young adults. New Media & Society,

13(5), 824-842.

Hatcher, L., & Stepanski, E. J. (1994). A step-by-step approach to using the SAS system

for univariate and multivariate statistics. Cary, NC: SAS Institute.

Haytko, D. L., & Baker, J. (2004). It’s all at the mall: exploring adolescent girls’

experiences. Journal of Retailing, 80(1), 67-83.

73

Ho, C. H. (2010). Continuance intention of e-learning platform: Toward and integrated

model. International Journal of Electronic Business Management, 8(3), 206-215.

Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online.

Communications of the ACM, 42(4), 80–85.

Hsu, M., & Chiu, C. (2004). Predicting electronic service continuance with a decomposed

theory of planned behaviour. Behaviour & Information Technology, 23(51),

359-373.

Hsu, C. L., & Wu, C. C. (2011). Understanding users’ continuance of Facebook: An

integrated model with the unified theory of acceptance and use of technology,

Expectation disconfirmation model, and flow theory. International Journal of

virtual communities and social networking (IJVCSN), 3(2), 1-16.

Huang, C. Y., Kao, Y. S., Wu, M. J., & Tzeng, G. H. (2013). Deriving factors influencing

the acceptance of Pad Phones by using the DNP based UTAUT2 framework.

Proceedings of PICMET 2013 (pp.880-887). San Jose, CA.

Hunt, H. K. (1977). CS/D-overview and future research directions. In H. K. Hunt (Ed.),

Conceptualization and measurement of consumer satisfaction and dissatisfaction.

Cambridge, MA: Marketing Science Institute.

Hunt, S. D., Sparkman Jr., R. D., & Wilcox, J. B. (1982). The pretest in survey research:

Issues and preliminary findings. Journal of Marketing Research, 19(2), 269-273.

Ibrahim, Y. (2008). The new risk communities: Social networking sites and risk.

International Journal of Media & Cultural Politics, 4(2), 245-253.

Internet World Stats (2012). Retrieved from http://www.internetworldstats.com/stats2.htm

74

Jarvenpaa, S. L., & Tractinsky, N. (1999). Consumer trust in an Internet store: A

cross-cultural validation. Journal of Computer Mediated Communication, 5(2),

1-36.

Kelly, H. (2013). Facebook changes privacy settings for teens. Retrieved from

http://www.cnn.com/2013/10/16/tech/social-media/facebook-teens-privacy/

Kenny, D. A. (2012). Measuring model fit. Retrieved from

http://www.davidakenny.net/cm/fit.htm

Kim, S. S., & Malhotra, N. K. (2005). A longitudinal model of continued IS use: An

integrative view of four mechanisms underlying post-adoption phenomena.

Management Science, 51(5), 741-755.

Kline, R. B. (2011). Principles and practice of structural equation modeling (3rd edition).

New York: The Guilford Press.

Krasnova, H., Spiekermann, S., Koroleva, K., & Hildebrand, T. (2010). Online social

networks: Why we disclose. Journal of Information Technology, 25(2), 109-125.

LaTour, S. A., & Peat, N. C. (1980). The role of situationally-produced expectations,

others’ experiences, and prior experience in determining consumer satisfaction. In J.

C. Olson (Ed.), Advances in Consumer Research (pp. 588-592). Ann Arbor, MI:

Association for Consumer Research.

Leonard, H. (2013). Americans Still Spend Far More Time On Facebook Than Other

Social Media. Retrieved from

http://www.businessinsider.com/facebook-wins-in-social-media-time-spend-2013-3

75

Liao, C., Palvia, P., & Chen, J. (2009). Information technology adoption behavior life

cycle: Toward a Technology Continuance Theory (TCT). International Journal of

Information Management, 29(4), 309-320.

Limayem, M., Hirt, S. G., & Cheung, C. M. K. (2007). How habit limits the predictive

power of intentions: The case of IS continuance. MIS Quarterly, 31(4), 705-737.

Limayem, M., Khalifa, M., & Chin, W. W. (2004). Factors motivating software piracy: A

longitudinal study. IEEE Transactions on Engineering Management, 51(4),

414-425.

Lin, C. P., & Anol, B. (2008). Learning online social support: An investigation of network

information technology. CyberPsychology and Behavior, 11(3), 268-272.

Locke, J. (2007). The social contract. In J. E. Adler & C. Z. Elgin (Ed.), Philosophical

inquiry: Classic and contemporary readings (pp.653-663). Indianapolis, IN:

Hackett Publishing Company.

Lu, J., Wang, L., & Hayes, L. A. (2012). How do technology readiness, platform

functionality and trust influence C2C user satisfaction? Journal of Electronic

Commerce Research, 13(1), 50-69.

Macneil, I. R. (1974). The many futures of contracts. Southern California Law Review,

47(3), 691-816.

McCarthy, C. (2008). ComScore: Facebook is beating MySpace worldwide. Retrieved

from http://news.cnet.com/8301-13577_3-9973826-36.html

76

McKnight, D. H., Lankton, N., & Tripp, J. (2011, January). Social networking

information disclosure and continuance intention: A disconnect. In System

Sciences (HICSS), 2011 44th Hawaii International Conference on (pp. 1-10).

IEEE.

Metzger, M. J. (2004). Privacy, trust, and disclosure: Exploring barriers to electronic

commerce. Journal of Computer-Mediated Communication, 9(4). Retrieved from

http://jcmc.indiana.edu/vol9/issue4/metzger.html

Milne, G. R., & Gordon, M. E. (1993). Direct mail privacy-efficiency trade-offs within an

implied social contract framework. Journal of Public Policy and Marketing, 12(2),

206-215.

Nicholas, C (2010). At Last – The Full Story Of How Facebook Was Founded. Retrieved

from http://www.businessinsider.com/how-facebook-was-founded-2010-3?op=1

Oliver, R. L. (1980). A cognitive model for the antecedents and consequences of

satisfaction. Journal of Marketing Research, 17, 460-469.

Okazaki, S., Li, H., & Hirose, M. (2009). Consumer privacy concerns and preference for

degree of regulatory control. Journal of Advertising, 38(4), 63-77.

Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple

processes by which past behavior predicts future behavior. Psychological Bulletin,

124(1), 54-74.

Pan, Y., & Zinkhan, G. M. (2006). Exploring the impact of online privacy disclosures on

consumer trust. Journal of Retailing, 82(4), 331-338.

Papadopoulou, P. (2007). Applying virtual reality for trust-building e-commerce

environments. Virtual Reality, 11(2), 107-127.

77

Pegoraro, R. (2010). The latest Facebook fracas: Your privacy vs. its profit. Retrieve from

http://www.washingtonpost.com/wp-dyn/content/article/2010/04/02/AR201004020

0762.html

Phau, I., & Teah, M. (2009). Devil wears (counterfeit) Prada: A study of antecedents and

outcomes of attitudes towards counterfeits of luxury brands. Journal of Consumer

Marketing, 26(1), 15-27.

Piotrowski, C. (2012). Facebook: A bibliographic analysis of the PsycINFO database.

Journal of Instructional Psychology, 39(1), 63.

Quelch, J. A., & Jocz., K. A. (2008). Greater good: How good marketing makes for better

democracy. Cambridge, MA: Harvard Business Press.

Rachels, J., & Rachels, S. (2011). Elements of moral philosophy. New York:

McGraw-Hill.

Riley, P. (2006). The social contract and its critics. In M. Goldie & R. Wokler (Eds.), The

Cambridge history of eighteenth-century political thought (pp.347-375).

Cambridge, UK: Cambridge University Press.

Rousseau, J. J. (1971). The social contract. London: Penguin Books.

Ryu, K., Han, H., & Jang, S. S. (2010). Relationships among hedonic and utilitarian

values, satisfaction and behavioral intentions in the fast-casual restaurant industry.

International journal of contemporary hospitality management, 22(2), 416-432.

Schumacker, R. E., & Lomax, R.G. (2004). A beginner’s guide to structural equation

modeling. Mahwah, NJ: Lawrence Erlbaum Associates.

78

Shin, D.-H., Kim, Y. (2010). Effect of trust and privacy concerns on social networking: A

trust-based acceptance model for social networking systems. Paper presented at the

annual meeting of the International Communication Association. Retrieved from

http://citation.allacademic.com/meta/p402742_index.html

Sibona, C., & Choi, J. H. (2012). Factors affecting end-user satisfaction on Facebook.

Proceedings of the Sixth International AAAI Conference on Weblogs and Social

Media (pp. 575-578). Denver, CO.

Singh, K. (2007). Quantitative social research methods. Thousand Oaks, CA: Sage

Publications.

Slade, E. L., Williams, M., & Dwivedi, Y. (2013). An extension of the UTAUT 2 in a

healthcare context. UK Academy for Information Systems Conference Proceedings

2013 (paper 55). Oxford, United Kingdom.

Smith, C. (2010). Delete your Facebook account: ‘Quit Facebook day’ wants users to

leave. Retrieved from

http://www.huffingtonpost.com/2010/05/15/delete-facebook-account-q_n_576956.

html

Smith, J. (2009). December data is in: Facebook surpasses MySpace in US uniques.

Retrieved from

http://www.insidefacebook.com/2009/01/08/december-data-is-in-facebook-surpass

es-myspace-in-us-uniques/

Spreng, R. A., MacKenzie, S. B., & Olshavsky, R. W. (1996). A reexamination of the

determinants of consumer satisfaction. Journal of Marketing, 60(July), 15-32.

79

Stieger, S., Burger, C., Bohn, M., & Voracek, M. (2013). Who commits virtual identity

suicide? Differences in privacy concerns, Internet addiction, and personality

between Facebook users and quitters. Cyberpsychology, Behavior, and Social

Networking, 16(9), 629-634.

Tang, J. H., & Farn, C. K. (2005). The effect of interpersonal influence on softlifting

intention and behaviour. Journal of Business Ethics, 56(2), 149-161.

Tatham, M. (2013). For every hour online Americans spend 16 minutes on social

networks. Retrieved from

http://www.experian.com/blogs/marketing-forward/2013/04/18/for-every-hour-onli

ne-americans-spend-16-minutes-on-social-networks/

Thong J. Y. L., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on

the expectation-confirmation model for information technology continuance.

International Journal of Human-Computer Studies, 64(9), 799-810.

Tufekci, Z. (2008). Can you see me now? Audience and disclosure regulation in online

social network sites. Bulletin of Science, Technology & Society, 28(1), 20-36.

Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS

Quarterly, 28(4), 695-704.

Venkatesh, V. (2012). Models. Retrieved from

http://www.vvenkatesh.com/organizations/models.asp

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of

information technology: Extending the unified theory of acceptance and use of

technology. MIS Quarterly, 36(1), 157-178.

80

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of

information technology: Toward a unified view, MIS Quarterly, 27(3), 425-478.

Wang, H. W., & Wang, S. H. (2010). User acceptance of mobile Internet based on the

Unified Theory of Acceptance and Use of Technology: Investigating the

determinants and gender differences. Social Behavior & Personality: An

International Journal, 38(3), 415-426.

Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age

and gender differences in the acceptance of mobile learning. British Journal of

Educational Technology, 40(1), 92-118.

Weisberg, H., Krosnick, J. A., & Bowen, B. D. (1996). An introduction to survey research,

polling, and data analysis. Thousand Oaks, CA: Sage.

Whitley, B. E. (1997). Gender differences in computer-related attitudes and behavior: A

meta-analysis. Computers in Human Behavior, 13(1), 1-22.

WHOA (2009). 2009 cyberstalking statistics. Retrieved from

http://www.haltabuse.org/resources/stats/2009Statistics.pdf

Wolverton, T. (2010). Wolverton: New Facebook changes threaten privacy. Retrieved

from http://www.mercurynews.com/404/ci_20040400?source=404_14985800

Wortham, J. (2010). Facebook and privacy clash again. Retrieved from

http://query.nytimes.com/gst/fullpage.html?res=9D02E3D61431F935A35756C0A9

669D8B63&pagewanted=all

Wu, C. C., Huang, Y., & Hsu, C. L. (2014). Benevolence trust: a key determinant of user

continuance use of online social networks. Information Systems and e-Business

Management, 12(2), 189-211.

81

Yen, H. J. R., & Gwinner, K. P. (2003). Internet retail customer loyalty: The mediating

role of relational benefits. International Journal of Service Industry Management,

14(5), 483-500.

Zeithaml, V. A. (1988). Consumer perceptions of price, quality, and value: A means–end

model and synthesis of evidence. Journal of Marketing, 52(3), 2-22.

Zikmund, W. G., & Babin, B. J. (2009). Essentials of marketing research (4th edition).

Mason, OH: Thomson/South-Western.

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