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Predicting Consumer Intention to Adopt Electronic Payment Systems Using the Theory of Reasoned Action

Dissertation Manuscript

Submitted to Northcentral University

Graduate Faculty of the School of Business and Technology Management in Partial Fulfillment of the

Requirements for the Degree of

DOCTOR OF BUSINESS ADMINISTRATION

ANTHONY BARNES

Prescott Valley, Arizona

July 2014

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Approval Page

Predicting Consumer Intention to Adopt Electronic Payment Systems

Using the Theory of Reasoned Action

Anthony Barnes

Approved by:

_______________________________________________ ________________

Chair: Dr. Robert F. George, Ph.D. Date

Certified by:

_______________________________________________ ________________

Dean of School: Dr. Jim Dorris, Ph.D. Date

Abstract

Business-to-consumer electronic commerce has expanded rapidly, but it is still far from

reaching its potential. The success of electronic commerce is dependent on an effective

electronic payment system to facilitate the purchase of products and services between

customers and merchants. The lack of consumer confidence has negatively influenced

electronic commerce, the adoption of its payment systems, and its long-term profitability.

The purpose of this quantitative ex post facto study was to examine the relationship and

test the predictive strength between five predictors and one criterion variable.

Consumers’ propensity to trust, perceived privacy, perceived security, subjective norms,

and recognition of third party existence were the predictor variables, while consumer

intention to adopt an electronic payment system was the criterion variable. The theory of

reasoned action (TRA) was the framework used for a deeper understanding of consumer

behavior, attitudes, and intention towards the adoption of electronic commerce

technology. Participants included a random sample of 197 online consumers, 18 years of

age or older, residing in the United States. Pearson’s correlation and linear regression

analysis were used to examine the relationship and test the predictive strength of the

variables. The results of this study indicated the five predictor variables showed a

significant and positive relationship towards the criterion variable. Likewise, the findings

also indicated the same five variables were significant predictors in consumer intention.

The strongest relationship among the predictors and criterion variable existed between

consumers’ propensity to trust (r = .626), perceived security (r = .597), and subjective

norms (r = .579), followed by the recognition of third party existence (r = .482) and

perceived privacy (r = .342, p < .001). The information obtained from this study may

provide online merchants with an understanding of consumer confidence concerns.

Online merchants may find opportunities to remove barriers and improve their strategies

to possibly change the attitudes of potential consumers and increase their intention to

adopt an electronic payment system. Recommendations for future research include

conducting a longitudinal study to examine consumer behavior over time, examining

prepurchase and postpurchase consumer experience, replicating the study using specific

demographic criteria, and employing a qualitative methodology.

iii

Table of Contents

Chapter 1: Introduction ....................................................................................................... 1

Background ......................................................................................................................... 3 Statement of the Problem .................................................................................................... 5 Purpose of the Study ........................................................................................................... 6 Research Questions ............................................................................................................. 7 Nature of the Study ........................................................................................................... 12 Significance of the Study .................................................................................................. 15 Definitions of Key Terms ................................................................................................. 16 Summary ........................................................................................................................... 21

Chapter 2: Literature Review ............................................................................................ 23

Documentation .................................................................................................................. 24 Online Consumer Trust ..................................................................................................... 24 Perceived Privacy.............................................................................................................. 34 Perceived Security ............................................................................................................ 41 Recognition of Third Party Existence ............................................................................... 47 Electronic Payment Systems ............................................................................................. 51 Subjective Norms .............................................................................................................. 63 Theory of Reasoned Action .............................................................................................. 66 Summary ........................................................................................................................... 74

Chapter 3: Research Method ............................................................................................. 77

Research Method and Design ........................................................................................... 85 Population ......................................................................................................................... 88 Sample............................................................................................................................... 90 Materials/Instruments ....................................................................................................... 94 Operational Definition of Variables ................................................................................ 101 Data Collection, Processing, and Analysis ..................................................................... 104 Assumptions .................................................................................................................... 109 Limitations ...................................................................................................................... 111 Delimitations ................................................................................................................... 113 Ethical Assurances .......................................................................................................... 113 Summary ......................................................................................................................... 116

Chapter 4: Findings ......................................................................................................... 118

Results ............................................................................................................................. 119 Evaluation of Findings .................................................................................................... 141 Summary ......................................................................................................................... 149

Chapter 5 Implications, Recommendations, and Conclusions ....................................... 150

Limitations ...................................................................................................................... 151 Implications..................................................................................................................... 153 Recommendations ........................................................................................................... 172

Conclusions ..................................................................................................................... 175

References ....................................................................................................................... 177

Appendixes ..................................................................................................................... 194

Appendix A: Cover Letter .............................................................................................. 195

Appendix B: Survey Instructions .................................................................................... 196

Appendix C: Informed Consent Form ............................................................................ 197

Appendix D: CTIS Survey Instrument ........................................................................... 198

Appendix E: IPI Survey Instrument ................................................................................ 202

Appendix F: Demographics Questionnaire ..................................................................... 206

Appendix G: Permission to Use Survey Instruments ..................................................... 207

Appendix H: Northcentral University IRB Approval ..................................................... 210

Appendix I: SurveyMonkey Audience Services ............................................................. 212

List of Tables

Table 1 Frequencies and Percentages for Demographic Characteristics (N = 197) .......... 121 Table 2 Reliability Statistics of Variables ............................................................................ 122 Table 3 Descriptive Statistics for Composite Scores (N = 197) .......................................... 122 Table 4 Pearson’s Inter-Item Correlation Matrix (N = 197) ............................................... 124 Table 5 Results from the Linear Regression Analysis (N = 197) ......................................... 136 Table 6 Results of Examining the Relationships Among Variables (N = 197) .................... 142 Table 7 Results of Testing the Predictive Strength Among Variables (N =197) .................. 142

List of Figures Figure 1. Research model of five predictors and one criterion variable. ............................... 86 Figure 2. P-P plot of regression for consumers’ propensity to trust. ................................... 126 Figure 3. Scatterplot of consumers’ propensity to trust. ..................................................... 126 Figure 4. P-P plot of regression for consumers’ perceived privacy. ................................... 128 Figure 5. Scatterplot of consumers’ perceived privacy. ...................................................... 128 Figure 6. P-P Plot of consumers’ perceived security. ......................................................... 130 Figure 7. Scatterplot of consumers’ perceived security. ..................................................... 130 Figure 8. P-P Plot for subjective norms............................................................................... 132 Figure 9. Scatterplot for subjective norms. ......................................................................... 132 Figure 10. P-P Plot of consumers’ recognition of third party existence. ............................ 134 Figure 11. Scatterplot of consumers’ recognition of third party existence. ........................ 134 Figure 12. P-P Plot of consumer intention to adopt an electronic payment system. ........... 140 Figure 13. Histogram of consumer intention to adopt an electronic payment system. ....... 140 Figure 14. Scatterplot of consumer intention to adopt an electronic payment system. ....... 141

Chapter 1: Introduction

The rapid growth of electronic commerce has transformed traditional methods of

conducting business creating new opportunities for online merchants and consumers

(Chen & Sharma, 2011). In 2009, online retail sales within the United States grew to

$155 billion and was expected to reach $248 billion by 2014 (Ahrholdt, 2011; Chen &

Sharma, 2011). The growth of electronic commerce has led to an increase of electronic

payment systems, resulting in efficiency, convenience, and flexibility for consumers and

merchants (Heikkinen & Livarinen, 2011; Ozkan, Bindusara, & Hackney, 2010).

Consumer trust is a significant factor in determining the success of electronic

payment systems (Blockley & McDowell, 2010). Online merchants have implemented

various measures to protect consumers, such as privacy policies, web assurance, and

security policies, with third-party organizations often providing these services (Sinclair,

Simon, & Wilkes, 2010). Nevertheless, many online consumers are reluctant to provide

personal information regardless of the assurances provided by online merchants (Gupta,

Iyer, & Weisskirch, 2010; Rajamma, Paswan, & Hossain, 2009). When engaging in an

online transaction, consumers disclose personal information, such as email addresses,

credit card numbers, financial data, and personal habits or preferences (Boriz & No,

2011). Credit card fraud is also a concern for consumers regardless of the advantages of

electronic payment systems (Merschen, 2010; Ozkan, Bindusara, & Hackney, 2009).

Several authors have supported the use of the theory of reasoned action (TRA) to

examine consumer behavior and social influence when predicting information systems

(IS) and electronic commerce technology adoption (Al-Majali, 2011; Corno, 2011;

Heikkinen & Livarinen, 2011; Smith & Caruso, 2010). TRA is a parsimonious and

intuitive theory used to explain consumer behavior, social influence, and one’s intention

to participate in electronic commerce (Corno, 2011). Consumer behavior may be

predicted by one’s intention to engage in an activity (Bleakley & Hennessy, 2012; Cha,

2011). TRA was used as the framework to examine the relationship and test the

predictive strength between each of the five predictor variables and one criterion variable.

Consumers’ propensity to trust, perceived privacy, perceived security, subjective norms,

and recognition of third party existence were the predictor variables, while consumer

intention to adopt an electronic payment system was the criterion variable. Third party

organizations, such as VeriSign and HackerSafe, provide services that monitor and test

merchants’ websites for assurance, authentication, and security (Coetzee, 2013;

Merschen, 2010; Salmony, 2011). Third parties increase an online vendor’s integrity by

implementing privacy, security, and vulnerability programs to protect private and

financial information (Sinclair et al., 2010; Warrick & Stinson, 2009). Studies have

shown that online purchase intentions would increase for vendors when consumers

recognize third party existence (Fisher & Chu, 2009; Sinclair et al., 2010).

Experts in the field (Richetin, Perugini, Adjali, & Hurling, 2008; Yousafzai,

Foxall, & Pallister, 2010; Zhou, Dai, & Zhang, 2007) have indicated the need for further

study based on the variables under investigation, which have not been studied relative to

consumer intention to adopt an electronic payment system. Chapter 1 includes an

introduction to the proposed study, the topic’s background, problem, purpose, and the

variables under investigation. The chapter continues with the appropriate research

questions and hypotheses to guide this quantitative study. This chapter concludes with

the nature and significance of the study, definitions of key terms, and a brief summary.

Background

The evolution and rapid growth of electronic commerce has transformed old

methods of conducting business, thereby creating new opportunities for online merchants

and consumers (Chen & Sharma, 2011; Cheney, Hunt, Jacob, Porter, & Summers, 2012;

Leko, Stojanovic, & Menalo, 2013; Ozkan et al., 2010; Podobnik, Trzec, & Jezic, 2010;

Raja, Velmurgan, & Seetharaman, 2008; Salmony, 2011; Sun, 2010b; Williams &

Moons, 2010). Electronic commerce is successful when consumers trust the virtual

environment (Goles et al., 2009; Heikkinen & Livarinen, 2011; Kord et al., 2011;

McKnight, Carter, Thatcher, & Clay, 2011; Raja et al., 2008). For this reason,

researchers have sought to examine the factors affecting the behavioral dimension of trust

in electronic commerce (Kord et al., 2011). Online merchants have implemented various

measures to protect consumers, such as privacy policies, security measures, and web

assurance policies, with third-party organizations providing these services (Furnell, 2010;

Guynes, Wu, & Windsor, 2011; Hannah & Lybecker, 2010; Sinclair et al., 2010).

Business-to-consumer electronic commerce is dependent on an effective

electronic payment system (Blockley & McDowell, 2010; Carter, Shaupp, Hobbs, &

Campbell, 2011; Cheney et al., 2012; Leko et al., 2013). The payment system is the

foundation of a successful online business operation (Fakhraddin, Hashemi & Nargesi,

2012; Leko et al., 2013) in order to respond to changes in consumer socio-economic

trends (Moshref Javadi, Dolatabadi, Nourbakhsh, Poursaeedi, & Asadollahi, 2012; Raja

et al., 2008), which benefits both consumers and online merchants (Ozkan et al., 2010;

Salmony, 2011).

Online vendors invest a considerable amount of resources (i.e., financial, time,

and effort) to establish and maintain their websites (Ahrholdt, 2011; Hewitt, 2011; Huff,

Desilets, & Kane, 2010). The purpose for the use of electronic payment systems is to

facilitate the exchange of products and services between buyers and sellers (Cheney et

al., 2012; Teitelbaum & Lamberg, 2010), and the payment process should not be a barrier

to accomplishing that objective (Ebben, 2013; Scarle, Arnab, Dunwell, Petridis,

Protopsaltis, & De Freitas, 2012). Consumers may not engage in online purchases and

use electronic payment systems if their expectations are not met, resulting in online

merchants losing potential sales (Blockley & McDowell, 2010; Cheney et al., 2012;

Hannah & Lybecker, 2010; Ozkan et al., 2010).

Despite the investments in electronic payment systems (Hewitt, 2011), U.S.

consumers led the industry by writing 26 billion checks in 2008 (Leibbrandt, 2010). The

reliance on paper-based instruments can be attributed to the perceived insecurity and lack

of confidence towards the payment systems (Cheney et al., 2012; Heikkinen & Livarinen,

2011). The rate of growth for business-to-consumer electronic commerce has slowed

(Hannah & Lybecker, 2010; Ramanathen, 2010). For example, 30% of consumers

reported a decrease in Internet use and 25% stated they had stopped shopping online

(Ratchford & Barnhart, 2012), resulting in a low conversion rate of online visitors

making purchases (Ahrholdt, 2011). The most successful online businesses convert 8%

of its website visitors to purchasers, but most vendors are only able to convert 2 to 3%

(Park & Wang, 2013). The lack of consumer confidence affecting online purchase

intentions is one of the reasons for failure in electronic commerce (Blockley &

McDowell, 2010; Cheney et al., 2012; Heikkinen & Livarinen, 2011; Salo & Karjaluoto,

2007), limiting the adoption of its payment systems (Ozkan et al., 2010).

Statement of the Problem

In 2010, approximately 228 million adults had access to the Internet in the United

States (U.S. Census Bureau, 2012). The market for business-to-consumer electronic

commerce has expanded rapidly in the first decade of the 21st century, but is still far

from reaching its potential (Hannah & Lybecker, 2010; Leko et al., 2013; Moshref Javadi

et al., 2012; Valacich, 2012; Wu, Zhou, & Yuan, 2012). Consumers’ unwillingness to

trust and participate in online transactions has cost the U.S. retail industry approximately

$6.5 billion in lost sales annually (Rajamma et al., 2009). The lack of consumer

confidence is one of the reasons for failure in electronic commerce (Blockley &

McDowell, 2010), limiting the adoption of its payment systems (Cheney et al., 2012;

Ozkan et al., 2010) and affecting the long-term profitability of online businesses (Sun,

2010b; Valvi & Fragkos, 2012). Consequently, there is a knowledge gap in which further

research in the context of TRA has been recommended to improve the understanding of

predicting consumer intention, which is a determinant of online behavior to accept, use,

or adopt electronic commerce technology (Al-Majali, 2011; Heikkinen & Livarinen,

2011; Peslak, Ceccucci, & Sendall, 2010). It has been posited that consumer behavior

can be predicted by one’s intention and the influence of subjective norms to engage or

not engage in an activity (Bleakley & Hennessy, 2012; Cha, 2011; Moshref Javadi et al.,

2012). By addressing the problem, a better understanding of the relationship and

predictive strength of five predictor variables towards consumer intention to adopt an

electronic payment system may have important implications for consumers and online

merchants participating in business-to-consumer transactions in the United States. A

better understanding of consumers’ concerns could help online vendors improve their

online payment systems, increase consumer adoption and sales, and predict online

purchase intentions (Cheney et al., 2012; Gao & Wu, 2010; Hoehle, Scornavacca, &

Huff, 2012; Vanetti, 2010).

Purpose of the Study

The purpose of this quantitative ex post facto study was to examine the

relationship and test the predictive strength between each of the five predictor variables

and one criterion variable. Consumers’ propensity to trust, perceived privacy, perceived

security, subjective norms, and recognition of third party existence were the predictor

variables, while consumer intention to adopt an electronic payment system was the

criterion variable. Two previously published survey instruments were chosen for this

study because the context was specific to the constructs under investigation (Cheung &

Lee, 2001; Crespo & Rodriguez, 2008). The Consumer Trust in Internet Shopping

(CTIS) survey (Cheung & Lee, 2001) was used to measure consumers’ propensity to

trust, perceived privacy, and perceived security. Subjective norms (i.e., influence of

family, friends, and media) and consumer intention to adopt an electronic payment

system were measured by the Internet Purchasing Intention (IPI) survey (Crespo &

Rodriguez, 2008). The number of participants was determined by conducting a power

analysis (Faul, Erdfelder, Buchner, & Lang, 2009). A minimum of 92 consumers, 18

years of age and older, who conduct online business-to-consumer transactions in the

United States, were needed to participate. The survey host managed the solicitation of

participants through its SurveyMonkey Audience database. TRA theorists purport that

consumer behavior is governed by the intention to perform a specific behavior based on

one’s attitude and subjective norms (Awa, Nwibere, & Inyang, 2010; Cha, 2011).

Consumer behavior may be predicted by one’s intention and the influence from friends,

family, or media to engage in an activity (Bleakley & Hennessy, 2012; Li & Karahanna,

2012; Moshref Javadi et al., 2012; Vachon, 2011). TRA was used as the framework to

examine the relationship and test the predictive strength between each of the five

predictor variables and one criterion variable. The results of this study may provide

online merchants with information to improve their electronic payment systems, increase

consumer acceptance of this technology, and develop methods to sustain long-term

profitability (Cheney et al., 2012; Ozkan et al., 2010; Valvi & Fragkos, 2012).

Research Questions

The goal of this quantitative ex post facto study was to examine the relationship

and test the predictive strength between each of the five predictor variables and one

criterion variable. Consumers’ propensity to trust, perceived privacy, perceived security,

subjective norms, and recognition of third party existence were the predictor variables,

while consumer intention to adopt an electronic payment system was the criterion

variable. In order to develop a better understanding of the research problem, the

following research questions and hypotheses were used to guide this study. Questions

one through five were used to examine the relationship between each of the five variables

and the criterion variable, while questions six through 10 were utilized to test the

predictive strength of each of same five variables and the criterion variable.

Q1. To what extent, if any, is consumers’ propensity to trust an online merchant,

as measured by the CTIS, related to consumer intention to adopt an electronic payment

system, as measured by the IPI for consumers 18 years of age or older conducting

business-to-consumer transactions?

Q2. To what extent, if any, is consumers’ perceived privacy, as measured by the

CTIS, related to consumer intention to adopt an electronic payment system, as measured

by the IPI for consumers 18 years of age or older conducting business-to-consumer

transactions?

Q3. To what extent, if any, is consumers’ perceived security, as measured by the

CTIS, related to consumer intention to adopt an electronic payment system, as measured

by the IPI for consumers 18 years of age or older conducting business-to-consumer

transactions?

Q4. To what extent, if any, is the average score for subjective norms (i.e.,

influence of family, friends, and media), related to consumer intention to adopt an

electronic payment system, as measured by the IPI for consumers 18 years of age or older

conducting business-to-consumer transactions?

Q5. To what extent, if any, is consumers’ recognition of a third-party existence,

as measured by the CTIS, related to consumer intention to adopt an electronic payment

system, as measured by the IPI for consumers 18 years of age or older conducting

business-to-consumer transactions?

Q6. To what extent, if any, does consumers’ propensity to trust an online

merchant, as measured by the CTIS, predict consumer intention to adopt an electronic

payment system, as measured by the IPI for consumers 18 years of age or older

conducting business-to-consumer transactions?

Q7. To what extent, if any, does perceived privacy, as measured by the CTIS,

predict consumer intention to adopt an electronic payment system, as measured by the IPI

for consumers 18 years of age or older conducting business-to-consumer transactions?

Q8. To what extent, if any, does perceived security, as measured by the CTIS,

predict consumer intention to adopt an electronic payment system, as measured by the IPI

for consumers 18 years of age or older conducting business-to-consumer transactions?

Q9. To what extent, if any, does the average score for subjective norms (i.e.,

influence of family, friends, and media), predict consumer intention to adopt an

electronic payment system, as measured by the IPI for consumers 18 years of age or older

conducting business-to-consumer transactions?

Q10. To what extent, if any, does consumers’ recognition of third-party

existence, as measured by the CTIS, predict consumer intention to adopt an electronic

payment system, as measured by the IPI for consumers 18 years of age or older

conducting business-to-consumer transactions?

Hypotheses

H10. Consumers’ propensity to trust an online merchant, as measured by the

CTIS, is not related to consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H1a. Consumers’ propensity to trust an online merchant, as measured by the

CTIS, is related to consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H20. Consumers’ perceived privacy, as measured by the CTIS, is not related to

consumer intention to adopt an electronic payment system, as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H2a. Consumers’ perceived privacy, as measured by the CTIS, is related to

consumer intention to adopt an electronic payment system, as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H30. Consumers’ perceived security, as measured by the CTIS, is not related to

consumer intention to adopt an electronic payment system for business-to-consumer

transactions, as measured by the IPI for consumers 18 years of age or older conducting

business-to-consumer transactions.

H3a. Consumers’ perceived security, as measured by the CTIS, is related to

consumer intention to adopt an electronic payment system as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H40. The average score for subjective norms (i.e., influence from family, friends,

and media), is not related to consumer intention to adopt an electronic payment system,

as measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H4a. The average score for subjective norms (i.e., influence from family, friends,

and media), is related to consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H50. Consumers’ recognition of third-party existence, as measured by the CTIS,

is not related to consumer intention to adopt an electronic payment system, as measured

by the IPI for consumers 18 years of age or older conducting business-to-consumer

transactions.

H5a. Consumers’ recognition of third-party existence, as measured by the CTIS,

is related to consumer intention to adopt an electronic payment system, as measured by

the IPI for consumers 18 years of age or older conducting business-to-consumer

transactions.

H60. Consumers’ propensity to trust an online merchant, as measured by the

CTIS, will not predict consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H6a. Consumers’ propensity to trust an online merchant, as measured by the

CTIS, will predict consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H70. Consumers’ perceived privacy, as measured by the CTIS, will not predict

consumer intention to adopt an electronic payment system, as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H7a. Consumers’ perceived privacy, as measured by the CTIS, will predict

consumer intention to adopt an electronic payment system, as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H80. Consumers’ perceived security, as measured by the CTIS, will not predict

consumer intention to adopt an electronic payment system, as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H8a. Consumers’ perceived security, as measured by the CTIS, will predict

consumer intention to adopt an electronic payment system, as measured by the IPI for

consumers 18 years of age or older conducting business-to-consumer transactions.

H90. The average score for subjective norms (i.e., influence from family, friends,

and media) will not predict consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H9a. The average score for subjective norms (i.e., influence from family, friends,

and media) will predict consumer intention to adopt an electronic payment system, as

measured by the IPI for consumers 18 years of age or older conducting business-to-

consumer transactions.

H100. Consumers’ recognition of third-party existence, as measured by the CTIS,

will not predict consumer intention to adopt an electronic payment system, as measured

by the IPI for consumers 18 years of age or older conducting business-to-consumer

transactions.

H10a. Consumers’ recognition of third-party existence, as measured by the CTIS,

will predict consumer intention to adopt an electronic payment system, as measured by

the IPI for consumers 18 years of age or older conducting business-to-consumer

transactions.

Nature of the Study

The purpose of this quantitative ex post facto study was to examine the

relationship and test the predictive strength between each of the five predictor variables

and one criterion variable. Consumers’ propensity to trust, perceived privacy, perceived

security, subjective norms, and recognition of third party existence were the predictor

variables, while consumer intention to adopt an electronic payment system was the

criterion variable. A quantitative methodology was appropriate for this study in order to

provide a predictive analysis among the variables under investigation (Cozby, 2009;

Nathans, Oswald, & Nimon, 2012; StatSoft, 2013; Tabachnick & Fidell, 2013; Vogt,

Gardner, & Haeffele, 2012). The proposed study’s variables are quantifiable and were

not controlled or experimentally manipulated (Fowler, 2009; Tabachnick & Fidell, 2013;

Vogt et al., 2012).

The variables under consideration were a retrospective examination of consumer

behavior that has occurred in the past. Consumers’ previous online shopping experience

may have a significant influence on their future purchasing intentions (Ling, Chai, &

Piew, 2010). The goal was to obtain the perspectives of individuals who have conducted

business-to-consumer transactions through an online merchant’s electronic payment

system (Kim, Ferrin, & Rao, 2009; Leedy & Ormrod, 2010; Podobnik et al., 2010). Data

were collected through an online survey.

The online survey host managed the solicitation of participants for this study

through its SurveyMonkey Audience database, which includes a pool of over 30 million

panel members residing in the United States. The target population for this study

consisted of adult online consumers, 18 years of age and older participating in business-

to-consumer transactions in the United States. A self-administered questionnaire was

distributed through SurveyMonkey to collect the data required to evaluate the research

questions and to test the associated hypotheses. The CTIS (Cheung & Lee, 2001) was

used to measure consumers’ propensity to trust, perceived privacy, perceived security,

and recognition of third-party existence. The IPI (Crespo & Rodriguez, 2008) was used

to measure subjective norms and the intention to adopt an electronic payment system.

Permission to use the survey instruments for this study was requested and received from

the publishers. The survey instrument also included five demographic questions (i.e., age

range, race, gender, education level, and geographic location) to describe the study’s

population. Consumers’ propensity to trust, perceived privacy, perceived security,

subjective norms, recognition of third-party existence, and the intention to adopt an

electronic payment system were measured by obtaining the participants’ subjective

opinions with answers provided on a 66-item survey instrument using a 7-point Likert

scale ranging from 1 (strongly disagree) to 7 (strongly agree).

The Cronbach’s alpha coefficient was used to assess the reliability and internal

consistency of the survey instruments (Gadermann, Guhn, & Zumbo, 2012; Nathans et

al., 2012; Tabachnick & Fidell, 2013; Vogt, 2007). Because the subscales of the

instrument contained less than 10 items, inter-item correlation was used to report the

reliability for each of the subscales (Attar & Sweiss, 2010; Diamantopoulos, Sarstedt, &

Fuchs, 2012). Pearson’s product-moment correlation and linear regression analysis were

applied to examine the relationship and test the predictive strength between the five

predictors and one criterion variable. Descriptive statistics were reported in order to

summarize and describe the collected data. The descriptive analysis information included

the means, standard deviations, and range of total scores for the variables under

investigation. The demographic data are presented through frequency tables, descriptive

statistical tables, and graphs.

Significance of the Study

This study was significant for both consumers and merchants participating in

online business-to-consumer activities. The gap in literature has shown that consumer

intention to adopt an electronic payment system had not been studied with the constructs

under examination (He & Mykytyn, 2007; Richetin et al., 2008; Yousafzai et al., 2010).

Authors have recommended that future research was needed to examine online consumer

behavior and intention (Crespo & Rodriguez, 2008; He & Mykytyn, 2007; Richetin et al.,

2008; Yousafzai et al., 2010; Zhou et al., 2007). The prediction of consumer intention

was found to be an important determinant to understand consumer behavior (Al-Majali,

2011; Alsajjan & Dennis, 2010; Ozkan et al., 2010). The information obtained from the

study may provide online merchants with a better understanding of consumer confidence

concerns (Blockley & McDowell, 2010; Guynes et al., 2011; Kord et al., 2011; Moshref

Javadi et al., 2012; Nicoleta, Racolta, & Luca, 2010; Sinclair et al., 2010; Sun, 2010b).

Furthermore, online merchants may find opportunities to improve their strategies to

possibly change the attitudes of consumers, remove barriers, and increase consumer

intention to adopt an electronic payment system (Cheney et al., 2012; He & Mykytyn,

2007; Heikkinen & Livarinen, 2011; Peslak et al., 2010; Ozkan et al., 2010; Raja et al.,

2008; Salmony, 2011; Scarle et al., 2012; Zhao & Zhao, 2012).

The results from this study may also add valuable knowledge to the electronic

commerce literature by examining how consumers’ propensity to trust, perceived privacy,

perceived security, subjective norms, and recognition of third party existence may

influence consumer intention to adopt an electronic payment system. The consequences

of not conducting this study may result in online merchants not fully understanding the

barriers preventing consumers from fully accepting the use of their payment systems

when participating in business-to-consumer transactions (Cheney et al., 2012; He &

Mykytyn, 2007; Milkau, 2010; Tsarenko & Tojib, 2009; Zhao & Zhao, 2012). Further,

consumers may not realize the associated benefits, such as time and cost efficiency,

convenience, and flexibility, provided by a merchant’s online payment system (Cheney et

al., 2012; Hannah & Lybecker, 2010; He & Mykytyn, 2007; Heikkinen & Livarinen,

2011; Hewitt, 2011; Leko et al., 2013; Moshref Javadi et al., 2012; Ozkan et al., 2009;

Salmony, 2011; Tsarenko & Tojib, 2009).

Definitions of Key Terms

This section is a brief discussion of critical and unique terms used in the proposed

quantitative survey research. Peer-reviewed literature and scholarly articles were used to

develop the definitions containing specific and key terms for this ongoing research.

Defining key words ensures the readers understand the specific terms used throughout the

study.

Authentication. Authentication refers to the ability to identify an individual or

organizations that conduct online transactions over the Internet. An example of an

authenticity violation occurs when someone uses a fraudulent e-mail address or poses as

someone else to conduct online transactions (Merschen, 2010). Electronic commerce

sites require authentication in the form of user names and passwords to ensure privacy

(Awa et al., 2010; Heikkinen & Livarinen, 2011).

Business-to-consumer. Business-to-consumer refers to electronic commerce

retail transactions of products and services between online merchants and end-user

consumers (Kord, Yaghoubi, Khani, & Esmaeali, 2011).

Confidentiality. Confidentiality is the ability to ensure that customer information

is available only to authorized viewers. An example of a confidentiality violation is

when a theft of proprietary information stored on a network system that may include e-

mail messages, company information, or confidential data (Blockley & McDowell,

2010).

Electronic commerce. Electronic commerce is a technological and business

innovation with enhanced methods of communication (Awa et al., 2010). Electronic

commerce is the process of conducting business transactions electronically. The

elements of electronic commerce include marketing, distributing, buying, or selling

products, information, or services primarily through the Internet (Oudan, 2010; Sun,

2010a; Yang, Chandlrees, Lin, & Chao, 2009).

Electronic payment system. An electronic payment system is used for monetary

exchange conducted through a merchant’s website for products and services (Leibbrandt,

2010; Leko et al., 2013). This technology allows merchants to provide payment services

that use information and communication technologies (Raja et al., 2008) to facilitate the

process of electronic payment for online transactions (Khoshnampour & Nosrat, 2011).

Encryption. Encryption refers to the process of encoding information, thereby

enabling users to protect sensitive data transmitted through digital networks. Encryption

technology enhances the security of information to prevent unauthorized access by

parties without the proper codes to decode messages. In this context, credit card

companies, such as Visa and MasterCard, use secure electronic transaction (SET) for

encryption and decryption methods to protect credit card information during an online

payment transaction (Leko et al., 2013; Merschen, 2010).

Fraud. This term describes any criminal scheme that uses information from a

payment transaction provided by credit cards, debit cards, online payments, and

automated clearinghouse transactions (Boritz & No, 2011). The consumer (first party) or

individuals operating in financial institutions (third party) can commit fraud (Cheney et

al., 2012; Gates & Jacob, 2009).

Perceived integrity. Perceived integrity refers to consumer awareness of online

merchants’ honesty when conducting online transactions (Yaghoubi et al., 2011).

Integrity means the ability to prevent unauthorized individuals from altering data

transmitted or received electronically (Laudon & Traver, 2011). Integrity describes how

consumers perceive online merchants compliance with a set of accepted principles

resulting in online merchants following through with promises (Merschen, 2010).

Perceived privacy. Perceived privacy describes the probability that online

merchants collect personal information about consumers and use this information

inappropriately (Furnell, 2010). Consumers are reluctant to provide personal information

when prompted in the checkout process of an online transaction because of the concerns

of data interception and misuse of information sent digitally (Boritz & No, 2011; Roca,

Garcia, & Dela Vega, 2009). Perceived privacy depicts the online consumers’

understanding of how online vendors can protect consumer information collected during

online transactions (Yaghoubi et al., 2011).

Perceived security. Perceived security implies the consumers’ awareness of an

online merchant’s ability to fulfill security measures in an online environment. The

security measures include authentication, integrity, and encryption (Guynes et al., 2011;

Laudon & Traver, 2011). For example, threats to security control include any potential

events that disrupt information or network resources through destruction, corruption of

data, denial of service, or fraud (Zhao & Zhao, 2012).

Secure Socket Layer (SSL). SSL is the most common form of encryption used

in e-commerce transactions in order to protect Internet connection lines (Kumar &

Raheja, 2012; Laudon & Traver, 2011; MacEwan, 2013). For example, when using SSL

technology, consumers will notice the browser contains the https protocol instead of the

http protocol in order to provide confidentiality during transactions on a merchant’s

website (Dinesha & Agrawal, 2013; Moshref Javadi et al., 2012).

Shopping cart. Electronic shopping cart means the virtual space similar

traditional shopping carts used to store products for subsequent purchase during the

shopping session (Kukar-Kinney & Close, 2010). The merchant server software provides

electronic shopping carts for customers to select products and review the selection

(Laudon & Traver, 2011; Vachon, 2011).

Shopping cart abandonment. Shopping cart abandonment refers to the point

right after the customer has decided to buy the products, but before completing the

purchase (Paden & Stell, 2010; Park & Wang, 2013).

Subjective norms. In the context of electronic commerce adoption, this term

represents an individual’s motivation to perform a behavior according to the opinion of

others (Al-Majali, 2011; Cha, 2011; Moshref Javadi et al., 2012). Subjective norms are

described as social influence that has been conceptualized into three dimensions that

include (a) family, (b) friends, and (c) media. Social influence is considered to have a

significant effect on an individual’s intention to accept or adopt electronic commerce (Al-

Majali, 2011; Crespo & Rodriguez, 2008; Sinclair et al., 2010).

Third party existence. Third party existence describes the assurances provided

by third party organizations, which is typically a trademark or symbol used to specify an

online business has met the standards to operate an electronic commerce business.

Examples of organizations providing web assurance for consumer use include WebTrust,

SysTrust, and TRUSTe (Boritz & No, 2011; Warrick, & Stinson, 2009). Third-party

services improve website reliability to instill consumer confidence in an online retailer

(Sinclair et al., 2010; Zhou et al., 2007).

Threat. Threat denotes the intent to adversely harm or damage an electronic

commerce system. To differentiate the term threat and vulnerability, vulnerability refers

to the inherent state of a physical, technical, or organizational system. During online

transactions between consumers and merchants, a threat within a vulnerable system

causes risk (Haimes, 2010).

Transaction security. This term is a technical component of an online merchant

and indicates the guarantee on behalf of the merchant to protect consumer information in

accordance with legal requirements and ethical practices (Leko et al., 2013).

Trust. Trust refers to the consumers’ attitude toward a merchant’s electronic

payment structure as a secure system, which is highly influential to online consumer

behavior (Brengman & Karimov, 2012; Mangiaracina, & Perego, 2009). Propensity to

trust is a behavioral characteristic that influences the probability that individuals will trust

one another. The factors influencing a person’s trust propensity includes culture,

personality, and experience (Yaghoubi et al., 2011).

Vulnerability. Vulnerability refers to the weakness of an IS in which

unauthorized individuals can attack, exploit, and gain access (Liu & Cheng, 2009;

MacEwan, 2013). Payment systems are vulnerable to fraud at any stage in the payment

chain. Oftentimes, fraudsters attempt to exploit the weakest part in that chain (Coetzee,

2013; Furnell, 2010; Gates & Jacob, 2009).

Summary

This quantitative ex post facto study involved adult consumers participating in

online business-to-consumer transactions in the United States. The lack of trust

(Heikkinen & Livarinen, 2011), perceived privacy (Blockley & McDowell, 2010),

perceived security (Carter et al., 2011), subjective norms (Alsajjan & Dennis, 2010), and

recognition of third party existence (Sinclair et al., 2010) are important factors that

influence consumer confidence towards online merchants (McKnight et al., 2011; Sellen

& Belczyk, 2011). The purpose of this quantitative ex post facto study was to examine

the relationship and test the predictive strength between each of the five predictor

variables and one criterion variable. Consumers’ propensity to trust, perceived privacy,

perceived security, subjective norms, and recognition of third party existence were the

predictor variables, while consumer intention to adopt an electronic payment system was

the criterion variable. TRA was the theoretical framework used to address consumer

intention and subjective norms (Al-Majali, 2011; Corno, 2011).

Presented in Chapter 1 was an overview, which included the introduction,

background, problem, purpose, research questions and hypotheses, nature and

significance of the study, and definitions of key terms. The findings of this study may

have important implications for consumers and online merchants participating in

business-to-consumer transactions in the United States. A better understanding of

consumer concerns may help online vendors improve their online payment systems,

increase consumer adoption and sales, and predict purchase intentions (Gao & Wu, 2010;

Vanetti, 2010).

Chapter 2: Literature Review

The purpose of this quantitative ex post facto study was to examine the

relationship and test the predictive strength between each of the five predictor variables

and one criterion variable. Consumers’ propensity to trust, perceived privacy, perceived

security, subjective norms, and recognition of third party existence were the predictor

variables, while consumer intention to adopt an electronic payment system was the

criterion variable. The number of participants was determined by conducting a power

analysis (Faul et al., 2009). A minimum of 92 consumers, 18 years of age and older, who

conduct online business-to-consumer transactions in the United States, were needed to

complete the online survey instrument. The Consumer Trust in Internet Shopping (CTIS)

survey (Cheung & Lee, 2001) was used to measure consumers’ propensity to trust,

perceived privacy, perceived security, and recognition of third party existence. The

Internet Purchasing Intention (IPI) survey (Crespo & Rodriguez, 2008) was used to

measure subjective norms and consumer intention to adopt an electronic payment system.

The remaining sections within this chapter include a discussion of the key areas in the

electronic commerce specialization and the search strategies used to perform the

literature review.

In the electronic commerce specialization, previous theoretical and conceptual

research has revealed several studies, which aid in the understanding of the relationship

between consumers and online merchants in six key areas. These key areas included

online consumer trust, perceived privacy, perceived security, subjective norms,

recognition of third-party existence, and electronic payment systems. This section also

includes a brief review to demonstrate how researchers have employed TRA to examine

disciplines involving the acceptance of IS and electronic commerce technology.

Documentation

Numerous research strategies were applied to obtain the appropriate sources and

literature. Secondary sources, such as books, were used to provide background

information in terms of quantitative methods and research design. The Northcentral

University (NCU) library’s Internet databases were selected for the literature search. The

databases included ABI/Inform, ACM Digital Library, AIS Library, Business Source

Premier, Emerald Full Text, Google Scholar, ProQuest, Psych ARTICLES, and Wiley.

A keyword search was conducted within the databases to identify relevant

scholarly and peer-reviewed papers. The keywords used in the literature search included

credit card authentication, business-to-consumer, consumer trust, credit card payments,

electronic commerce, electronic payment systems, electronic shopping cart, Internet

fraud, technology adoption, online consumer behavior, online privacy, online security,

and theory of reasoned action.

Online Consumer Trust

For the purpose of this research, it was necessary to review foundational studies to

examine the development of this topic from a historical perspective in order to

understand how consumer trust in an online environment emerged. Since 1997, the use

of the Internet and the World Wide Web has revolutionized how online business is

conducted (Nah & Davis, 2002). With the exponential growth of the Internet and its user

base, the potential for purchasing goods and services online remains untapped (Cheung &

Lee, 2001). However, when compared to traditional face-to face shopping, the electronic

business-to-consumer environment has caused constraints on the merchant and customer

relationship (Nah & Davis, 2002). One of the most cited constraints includes the lack of

consumer trust and its antecedents (i.e., trust propensity and trust intentions; Brengman &

Karimov, 2012; Cheung & Lee, 2001; Nah & Davis, 2002). In addition, it is important to

highlight the limitations of past research to determine how the topic of consumer trust has

evolved. For example, the limitations of trust in an online environment include the

absence of a clear definition of trust (Kim et al., 2009), theory-guided research (Cheung

& Lee, 2001), and validated instruments to measure trust in the field of electronic

commerce (Cheung & Lee, 2001; Ghandour, Benwell, & Deans, 2010; Holsapple &

Sasidharan, 2005).

The lack of a clear definition of trust was found to be a factor hindering the

understanding of consumer trust towards an online merchant (Brengman & Karimov,

2012; Kim et al., 2009; Wang, Chen, & Jiang, 2009). Experts in the field have defined

the term trust in organizational and electronic commerce seminal research, but defining

trust was noted to be an ongoing process (Kim et al., 2009; McKnight et al., 2011; Wang

et al., 2009). For instance, trust has been defined as the willingness to depend on an

exchange partner in whom one has faith (Tsarenko & Tojib, 2009), while Ling et al.

(2010) defined trust as the “willingness to accept weakness in an online transaction based

on their positive expectations regarding future online store behavior” (p. 66). Taddeo

(2009) also noted that the lack of a satisfactory definition of trust has created new

problems in a digital age. In Taddeo’s (2009) study, trust was defined as “a particular

level of subjective probability with which an agent assesses that another agent or group of

agents will perform a particular action” (p. 3). (The term agent represents an individual

[i.e., consumer] or an entity [i.e., online merchant].) In a recent study of electronic

business, Yaghoubi et al. (2011) also confirmed that there was not a consensus on the

definition of trust in the literature in either online or offline shopping. The lack of a

general definition or concept of trust exists mainly because trust is a part of human nature

with various understandings resulting from one’s experience (Ling et al., 2010; Yaghoubi

et al., 2011).

Although trust in electronic commerce is important for the success of business-to-

consumer electronic commerce, historically, there is little theory-guided research found

to understand the nature of trust and its antecedents (i.e., trust propensity, privacy, or

security; Brengman & Karimov, 2012; Cheung & Lee, 2001). To address the extent of

this problem and to fill this research gap, Cheung and Lee’s (2001) study was two-fold:

to provide theory-guided research and develop a practical instrument to measure online

shopping behavior. Cheung and Lee conducted an exploratory quantitative study to

capture the significant factors of trust related to Internet shopping (i.e., perceived

security, privacy, integrity, competence, personality, cultural environment, consumer

experience, third-party recognition, legal framework, trust in Internet shopping, and

perceived risk). Cheung and Lee’s study was conducted in China and a nonprobability

sampling method was used to select 405 college students as participants to complete a

survey. The researchers believed the students were representative of the population of

online shoppers. The 30-item survey instrument was presented in a Likert-type format to

measure consumers’ behavior in terms of consumer propensity to trust an online

merchant. The outcome of this study led to an empirically tested instrument, the CTIS,

that could be used as the foundation for further electronic commerce research (Cheung &

Lee, 2001; see also Connolly & Bannister, 2008).

A limitation of Cheung and Lee’s (2001) study was that the data were collected

from university students, whom the authors believed to be potential online shoppers. The

results indicated that more than 90% of the respondents did not have experience with

shopping online. This inexperience may explain why the factors of security control,

privacy control, legal implications, and third-party recognition were not significant in the

study (Cheung & Lee, 2001). A weakness of nonprobability convenience sampling is the

potential for researcher bias and weak external reliability (Vogt, 2007). Nevertheless, the

results of the CTIS may benefit online merchants by enhancing consumer trust and

improving the likelihood of consumers shopping from their websites (Cheung & Lee,

2001).

The CTIS was also used to provide the framework for other studies to examine

consumer trust of an online merchant (Borchers, 2001; Fazlollahi, 2002). In the only

study found that replicated Cheung and Lee’s (2001) study in the United States, Borchers

(2001) conducted a study with 118 students from a Midwestern university. This study

was important because Cheung and Lee’s (2001) study was conducted in China where the

Chinese government plays a much more critical role in consumer Internet activity

compared to the United States (Park & Wang, 2013). For example, the Chinese

government maintains a sophisticated Internet filtering system and prohibits certain

activities involving citizens’ access to the Internet. China has made efforts to develop the

Internet, but due to the large size of China, most of the infrastructure growth occurs in

urban areas. Therefore, rural areas are lacking in terms of Internet connectivity to sustain

online business (Park & Wang, 2013). The results of Borcher’s (2001) study showed

partial support for the CTIS, whereas the strongest relationship was between perceived

consumer risk and the CTIS. Upon further review of the data from Borcher’s (2001)

study, the use of a one-way ANOVA showed the influence of the respondent’s nationality

caused weak results (Borchers, 2001). The student participants were restricted to a

Midwest university student population, which was also a weakness in terms of

generalizability (Borchers, 2001). Students are not necessarily representative of the

general population (Junco, 2011; Park & Gretzel, 2010; Vogt et al., 2012). To verify the

applicability of these findings to a broader population, it was recommended that similar

research be conducted with a more demographically diverse sample (Borchers, 2001).

Between 2006 and 2011, several authors provided further validation of the CTIS to

demonstrate the problem of consumer trust involving online merchants still exists

(Connolly & Bannister, 2008; Flavian & Guinaliu, 2006; Kord et al., 2011; McKnight et

al., 2011; Yaghoubi et al., 2011).

Cheung and Lee (2006) also addressed consumer trust by using a

multidisciplinary approach to examine consumer’s perceived trustworthiness towards

online merchants (see also Wang, Zheng, Xu, Li, & Meng, 2008; Yaghoubi et al., 2011).

Examining trust at the group and the interpersonal level was found to be an important

factor to gain a better understanding of the expectations of shoppers when conducting

online transactions (Brengman & Karimov, 2012; Cheung & Lee, 2006). Brengman and

Karimov (2012) found three related attributes of trust, which included (a) ability (i.e., the

skills and competencies of the trustees), (b) benevolence (i.e., the extent to which a

trustee shows goodwill toward the trustor), and (c) integrity (i.e., the consistency and

reliability of the trustee’s previous actions). Trust, in general, was also determined to be

a factor in the social interaction between consumers and online businesses (Brengman &

Karimov, 2012; Lee et al., 2011; Sun, 2010a). For example, when viewing trust as an

institutional phenomenon, consumers must decide whether to trust an online business and

merchants they are unfamiliar with (Brengman & Karimov, 2012; Cheung & Lee, 2006).

Taddeo (2009) emphasized the problem of online consumer trust by researching

the historical aspects to resolve current problems in a digital environment. Taddeo’s

(2009) study was guided by Luhmanns’ analysis of trust, which included the previous 20

years of electronic commerce activities (Taddeo, 2009). In the early stages of business-

to-consumer electronic commerce, e-trust was described as taking place in a virtual

environment, which lacked physical presence or contact (Brengman & Karimov, 2012;

Coker, Ashill, & Hope, 2011; Taddeo, 2009). A lack of face-to-face interaction was a

significant factor, which influences consumer behavior towards trust and shopping over

the Internet (Kord et al., 2011; Moshref Javadi et al., 2012). To reduce the negative

concerns of consumers, Kim and Benbasat (2003) explained how Internet merchants

could instill consumer confidence by incorporating trust-related arguments (i.e.,

statements that provide support to enhance trust in Internet shopping). An example of a

trust related argument is, “Your online purchase is safe with us, we use advanced

encryption technology to protect your information and your privacy” (Kim & Benbasat,

2003, p. 49).

The lack of consumer trust has been observed as the most cited reason why

consumers are reluctant to buy goods and services through online vendors (Blockley &

McDowell, 2010; Cheney et al., 2012; Cheung & Lee, 2001; Heikkinen & Livarinen,

2011; McKnight et al., 2011; Salo & Karjaluoto, 2007; Sun, 2010b). The examples

illustrated above show that IS researchers depend on a multidisciplinary framework

because a single model or theory is sometimes inadequate to explain phenomena

concerning the complexities of human behavior (Parrish, 2010). Consequently, when

conducting online business-to-consumer transactions, trust is necessary for social

interaction and collaboration in an online environment (Brengman & Karimov, 2012;

Cheung & Lee, 2006; Lee et al., 2011; Sun, 2010a). Accordingly, the perspectives of the

social psychology principles (i.e., TRA; Aboelmaged, 2010; Al-Majali, 2011; Fishbein &

Ajzen, 1975) were used to guide the current study.

Electronic commerce is successful when consumers trust the virtual environment

(Goles et al., 2009; Kord et al., 2011; Ling et al., 2010). For this reason, researchers have

sought to examine the factors that affect the behavioral dimension of trust in electronic

commerce (Kord et al., 2011; Yaghoubi et al., 2011). Various empirical trust models

were compared to identify the main factors affecting consumer trust. The findings

showed that six dimensions of trust were the most relevant to explain consumer trust in

an electronic commerce environment: (a) customer behavior, (b) institutional, (c)

information content, (d) interaction, (e) products, and (f) technology (Yaghoubi et al.,

2011). In the study, the concepts of the CTIS were also examined. The results validated

the constructs of the CTIS were parsimonious and fully addressed the six dimensions

(Cheung & Lee, 2001; Yaghoubi et al., 2011). Similar to Cheung and Lee’s (2006)

study, Yaghoubi et al. (2011) suggested the use of a multidisciplinary approach for future

studies to develop improved initiatives to increase trust among consumers and merchants

(Yaghoubi et al., 2011) and resolve the problems concerning consumer confidence.

The problem based on the lack of consumer confidence resulted in online

shopper’s unwillingness to trust and participate in online transactions, costing the U.S.

retail industry $6.5 billion in lost sales annually (Rajamma, et al., 2009). Consumer trust

is an essential factor in online shopping (Goles et al., 2009; Sun, 2010a) and influences

the acceptance of online business-to-consumer transactions (Brengman & Karimov,

2012). Online trust can be characterized by uncertainty, anonymity, and a lack of control

on behalf of the consumer as well as possible opportunism (Goles et al., 2009; Kim, Kim,

& Hwang, 2009). Trust is a method of control that prevents opportunistic conduct,

encourages fulfillment of transactions, and develops long-term relationships between the

customer and the merchant (Kim et al., 2009; Sun, 2010b). An example of opportunism

occurs when online merchants are subject to the vulnerabilities of third party security

breaches (MacEwan, 2013; Malhotra & Malhotra, 2012).

From an opportunist perspective, the topic of trust violations was addressed

concerning online retailing as well as research on three characteristics of these violations

(Goles et al., 2009). These characteristics described the magnitude of the negative

outcome, causal attribution to the seller, and perceived fairness of the seller’s response.

The purpose of the study was to expand on the limited research on the consequences of

trust violations consumers experience when conducting online business-to-consumer

transactions (Goles et al., 2009). Trust violations, such as service failures or unmet

expectations in online retailing, were attributed to influencing a psychological contract

violation. Psychological contract violation stands for an affective or emotional state

resulting from the failure to meet expected obligations on behalf of a second party to a

psychological contract. Furthermore, the negative outcomes were described as the issue

that customers tended to place blame on the online merchant in terms of the perceived

fairness of vendor response (Goles et al., 2009).

Trust is a key factor in promoting online shopping and trust violations resulted in

negative consequences for the merchant involved. Incidents involving consumer

database breaches compromise the privacy of customers resulting in violations of trust

(Malhotra & Malhotra, 2011). For this reason, online merchants must decrease trust

violations and increase the consumer trust during online retail transactions (Goles et al.,

2009). The formation of trust involves a combination of factors to reduce the

complexities of consumer decision-making (Gao & Wu, 2010; McKnight et al., 2011).

The problem of consumer trust was determined to be an obstacle to successful

online transactions (Gao & Wu, 2010; Sinclair et al., 2010; Wang et al., 2009), which

hindered consumer confidence. To address the complexities of online consumer

behavior, Wang et al. (2009) developed an empirical study to determine the relationship

between knowledge, trust in Internet shopping, and the intention to shop online. In the

study, 251 college students were surveyed using a hard copy questionnaire and a 7-point

Likert scale to measure consumers’ perceived integrity, the level of trust propensity and

trust, knowledge, and awareness of security measures. The survey questionnaire was a

potential threat to validity because the common variance might produce a significant

effect when the actual effect was caused by the methodology used. The convergent

validly was acceptable because all factor loadings were significant and the confidence

intervals confirmed the discriminant validity. Then, the hypotheses were tested with

linear regression and structural equation modeling (SEM) and all hypotheses were

supported. The results indicated that 61% of the respondents had previous online

shopping experience and 31% of those individuals had previously provided credit card

information online. The participants were also concerned about online security and the

findings showed that user knowledge was associated with trust and Internet shopping

(Wang et al., 2009).

In contrast to Cheung and Lee’s (2001) study, consumer propensity to trust was

essential during the initial stages of building trust, but was not a significant factor for

experienced online consumers (Wang et al., 2009). Furthermore, research has shown that

online consumer’s experience level plays a role in online trust (Ling et al., 2010; Taddeo,

2009). For this reason, the factor of consumer experience was taken into consideration

for the current study (Cheung & Lee, 2001; Taddeo, 2009; Wang et al., 2009). Equally

important, Wang et al. (2009) provided valuable information to benefit electronic

commerce researchers and online merchants. Trust plays a leading role in the adoption of

electronic commerce portals (Heikkinen & Livarinen, 2011; Kim et al., 2009) and in

creating a satisfactory outcome during online transactions (Ling et al., 2010). Focusing

on the dynamics of trust (Wei, Lu, & Yanchun, 2008) may provide a better understanding

of consumer adoption of electronic commerce portals (Blockley & McDowell, 2010).

In terms of consumer adoption of electronic commerce, some studies have

indicated the rapid growth of electronic commerce (Chen & Sharm, 2011; Taddeo, 2009)

and maintaining consumer trust are significant for the success of the online marketplace

(Goles et al., 2009; Kord et al., 2011; Salmony, 2011). Nevertheless, the rate of growth

for business-to-consumer electronic commerce has slowed (Ramanathan, 2010;

Ratchford & Barnhart, 2012), resulting in a lower conversion rate of website visitors

making actual purchases (Ahrholdt, 2011). Researchers have shown that the lack of

consumer trust is a reason for electronic commerce failure (Rajamma et al., 2009; Salo &

Karjaluoto, 2007). Problems with consumer trust have been documented in terms of

affecting consumer confidence when using electronic commerce (Wang et al., 2009) and

its payment systems (Cheney et al., 2012; Ozkan et al., 2010). The implications from

these studies hold for the intended topic, indicating that consumer trust is a key predictor

for consumer adoption of electronic commerce and its payment systems (Cheung & Lee,

2001; Goles et al., 2009; Holsapple & Sasidharan, 2005; Kim et al., 2009; Kord et al.,

2011; Rajamma et al., 2009; Taddeo, 2009; Wang et al., 2009; Yaghoubi et al., 2011).

Based on the views of experts in the field of electronic commerce (Cheung & Lee,

2006; Wang et al., 2008; Yaghoubi et al., 2011), online consumer trust is a topic of

current interest to examine consumer intention to adopt an electronic payment system.

However, the lack of consumer trust in an online environment may be intensified with the

fear of losing personal information because of privacy concerns (Ozkan et al., 2010).

Perceived Privacy

Online shopper’s fear of losing personal or financial information was also found

to be a barrier to business-to-consumer electronic commerce (Cheung & Lee, 2001;

Coker et al., 2011; Federal Trade Commission, 2010; Hurwitz, 2011; Nah & Davis,

2002). Also associated with perceived privacy (Cheung & Lee, 2006; Lowe, 2013) is the

lack of control over the external environment and control over the use of consumer

information by second and third parties (Nah & Davis, 2002). In the seminal study, 95%

of online consumers declined to provide personal information during an online

transaction at one time or another (Nah & Davis, 2002). In order to improve consumer

confidence, researchers suggested that online merchants should incorporate privacy

policies (Yang et al., 2009), informed consent (Nah & Davis, 2002), and the use of third

party organizations (Warrick & Stinson, 2009) to establish reputable sites. The previous

discussion showed the problem of perceived privacy has been documented by early

studies (Cheung & Lee, 2001; Nah & Davis, 2002). A 2007 survey revealed that 85% of

mid- to large-sized companies have experienced a security breach involving customer

information. From 2009 to 2011, “Forrester Research has reported that more than 100

million personally identifiable customer records have been breached in the United

States…and that most of these breaches occurred at well-known companies” (Malhotra &

Malhotra, 2011, p. 44). For this reason, consumers are concerned about their privacy

because of the rapid growth of ID theft conducted through the Internet (Nicoleta et al.,

2010; Zhao & Zhao, 2012).

With advanced information technology and business-to-consumer activities,

online merchants are collecting more consumer personal information (Boritz & No, 2011;

Malhotra & Malhotra, 2011). As a result, online consumers must oftentimes submit a

great deal of personal information, such as credit card numbers, addresses, and telephone

numbers, to complete an online transaction (Mothersbaugh, Foxx, Beatty, & Wang, 2012;

Nicoleta et al., 2010). This is done before the completion of a transaction when the

products have been selected and placed in an electronic shopping cart. An electronic

shopping cart is a virtual space similar to traditional shopping carts that store products for

subsequent purchase during the shopping session (Kukar-Kinney & Close, 2010). The

following study is a related example of shopping cart abandonment.

Rajamma et al. (2009) addressed the factors resulting in shopping cart

abandonment by consumers in the course of online transactions. For the purpose of this

study, shopping cart abandonment refers to the point after the customer has selected

products to buy by placing them into the cart. However, the customer changes his or her

mind before completing the purchase (Kukar-Kinney & Close, 2010; Rajamma et al.,

2009) through a merchant’s payment system. Perceived privacy (Mothersbaugh et al.,

2012; Nicoleta et al., 2010) and security risks (Coker et al., 2011; Fakhraddin et al.,

2012) were identified as the contributing factors causing consumers to halt their

transactions at this point (Rajamma et al., 2009).

Since there was a lack of an existing scale to measure shopping cart abandonment,

the instrument used was developed from trade journals and exploratory research with

professionals from the field (Rajamma et al., 2009). As a result, a 14-item survey

instrument was developed to measure shopping cart abandonment with a 5-point Likert

scale ranging from 1 (strongly disagree) to 5 (strongly agree). The three predictor

variables included (a) perceived transaction inconvenience, (b) perceived risk, and (c)

perceived waiting time, with shopping cart abandonment as the outcome variable

(Rajamma et al., 2009). Convenient sampling techniques were initially employed to

recruit business students in attendance at two universities (Northeast and Southwest).

Then, a snowball sampling technique was used whereby each respondent was asked to

recruit at least two other individuals who had shopped online. An online self-

administered survey was utilized to collect the data from participants who had shopped

online at least once during the previous 12-month period. In this study, 733 surveys were

completed and 13 were discarded because the participants did not meet the criteria of

being online shoppers. Therefore, 720 surveys were used for this study (Rajamma et al.,

2009).

The survey results showed the sample age ranges and gender distributions were

comparable between both groups from the two universities (Rajamma et al., 2009). The

average number of shopping cart abandonment occurrences was 4.58 times during the

previous 12-month period. For internal consistency, Cronbach’s alpha was applied, with

the following results: perceived risk was .905, perceived waiting time was .813, and

perceived transaction inconvenience was rated as .692. The inter-item correlation

indicated an adequate level of convergent and discriminant validity. A logistic regression

statistical analysis was applied and the predictor variables were found to have significant

influences toward shopping cart abandonment. The findings indicated the problem of

perceived risk (p = 0.01), perceived waiting time (p = 0.044), and perceived transaction

inconvenience (p = 0.01) during online transactions significantly influenced consumers to

abandon their shopping cart (Rajamma et al., 2009). The methodology of this study was

useful in describing perceived privacy concerns (Mothersbaugh et al., 2012; Nicoleta et

al., 2010), which influences one’s intention to adopt an electronic payment system when

conducting business-to-consumer transactions through an online merchant (Raja et al.,

2008; Salmony, 2011).

Sharing personal information with online merchants is one of the tradeoffs to

conduct online transactions (Boritz & No, 2011). However, consumers remain hesitant to

disclose personal information to online vendors despite the support and assurance

provided by these retailers (Mothersbaugh et al., 2012; Nicoleta et al., 2010; Rajamma et

al., 2009). For instance, there are ethical issues involved with shopping websites, such as

the vendor’s misuse of consumer personal information, violations of privacy, and the

potential impact on consumer confidence. Examples of ethical issues consist of

misleading advertisements, untruthfulness, poor product quality, cheating, privacy

violations, personal information misuse, and trust betrayal (Eun-Jung & Park, 2010; Huff

et al., 2010; Yang et al., 2009). In an online environment, there is a close connection

between the concepts of privacy and trust (Yang et al., 2009). Therefore, privacy is

tantamount to trust whereas both terms are necessary components of a personal or

commercial relationship (Carter et al., 2011; Kord et al., 2011; Kovacs, Farias, Moura, &

Souza, 2011; Tsarenko & Tojib, 2009).

To address how online businesses could create and increase trustworthiness for

customers, an experimental study was conducted to investigate the constructs of privacy

and consumer trust (Yang et al., 2009). The results showed that consumer perceived trust

is a key predictor of the consumer’s intent to conduct online transactions. The study was

beneficial in helping merchants to understand the relationship between ethics and

customer confidence to overcome barriers to electronic commerce success (Yang et al.,

2009). Online retailers are collecting more information from customers for a better

understanding of how to service them. The manner in which this information is

collected, used, and protected is essential to online service quality (Malhotra & Malhotra,

2011; Mothersbaugh et al., 2012). Accordingly, the majority of online consumers

hesitate to provide requested information because of concerns about the use and control

of personal information (Coker et al., 2011; Federal Trade Commission, 2010; Hurwitz,

2011; Mothersbaugh et al., 2012). The sensitivity of information corresponds to

intimacy. For instance, the greater level of intimacy reflects the greater risk of disclosure

because of the vulnerability to loss or use of the information other than its intended

purpose (Heikkinen & Livarinen, 2011; Lee & Chen, 2010; MacEwan, 2013).

In an experimental study, Mothersbaugh et al. (2012) examined the antecedents of

online disclosure regarding the sensitivity of personal information. The prospect theory

was employed to investigate consumer’s willingness to disclose personally identifiable

information in online services, which is important for theory, research, and practical

applications. Three antecedents were examined in an online environment: (a) privacy

concerns, (b) perceived control over the use of information, and (c) the perceived benefits

of site customization (Mothersbaugh et al., 2012). A hypothetical online television

program guide (YOURTV) was developed to test six hypotheses. The study included

776 U.S. participants recruited by 341 graduate business students who were trained in

recruitment and sampling techniques. The participants were over 18 years old,

nonstudents, and Internet users. The demographics (i.e., age, gender, race, and marriage

status were reflective of the census percentages for U.S. Internet users (Mothersbaugh et

al., 2012). The authors used a structural equation modeling to test the hypotheses. The

results showed a high level of convergent validity with a significant factor load and

discriminant validity was confirmed by the confidence intervals (Mothersbaugh et al.,

2012). The sensitivity of personal information is an essential consideration for online

vendors who attempt to customize and tailor their services to online customers. The

authors recommended that future studies should involve investigation of disclosure

policies in regards to online security (Mothersbaugh et al., 2012). Examining online

consumer privacy is pertinent to the current study because of the importance of protecting

personal information from fraudulent crimes and mitigating the potential threat of

identity theft (Coetzee, 2013; Eun-Jung & Park, 2010; Huff et al., 2010; Merschen, 2010;

Mothersbaugh et al., 2012).

Consumer identity theft has continued to increase over the Internet (Nicoleta et

al., 2010). A National Consumer Complaint Report listed identity theft as the top

complaint for the 13th consecutive year (Federal Trade Commission, 2013). During

calendar year 2012, a report indicated that there were 369,312 identity theft complaints in

the United States (Federal Trade Commission, 2013). As a result, consumers are

reluctant to disclose personal information via the Internet regardless of the assurances

that online merchants provide (Zhao & Zhao, 2012). Comparatively, consumer trust is a

predictor of consumer willingness to transact with online merchants and consumers are

more likely to trust a website if privacy policies are explicitly stated (Boritz & No, 2011;

Yang et al., 2009). For this reason, merchants must have a better understanding of the

relationship between website ethics and consumer confidence to overcome the barriers to

maximize electronic commerce success (Goles et al., 2009; Kord et al., 2011). The

misuse of one’s personal information, privacy violations by the online retailer, or an

inability to protect this information from being exploited negatively influences consumer

confidence (Yang et al., 2009).

Perceived privacy is a predictor variable that was examined for its relationship

and predictive strength towards consumer intention to adopt an electronic payment

system (Coker et al., 2011; Kukar-Kinney & Close, 2010; Fakhraddin et al., 2012).

Consumer concerns with information privacy and the security of the consumer’s online

transaction are two impediments to shopping over the Internet (Malhotra & Malhotra,

2011; Mothersbaugh et al., 2012; Nicoleta et al., 2010; Zhao & Zhao, 2012). Therefore,

the third predictor variable to be reviewed is perceived security, which is also a key

determinant for consumers when conducting online business-to-consumer transactions.

Perceived Security

Consumers’ perceived security has also been identified as a barrier to the success

of electronic commerce (Anderson & Agarwal, 2010). Consumers are unwilling to

provide credit card information online because of the security risks involved with online

transactions (Zhao & Zhao, 2012). This may explain why consumers may frequently

shop in physical stores (Flavian & Guinaliu, 2006) or seek alternative payment methods

(Leibbrandt, 2010). Historically, payment instruments (i.e., credit cards) were developed

at a time when the security risks consumers experience today was unthinkable (Heikkinen

& Livarinen, 2011; Lowe, 2013). For example, in a 3 year span, 1995 to 1998, the total

number of known security vulnerabilities increased from 171 to 6,058 (Liu & Cheng,

2009). The term vulnerability refers to the weakness of an IS that unauthorized

individuals can attack, exploit, and gain access (Coetzee, 2013; Liu & Cheng, 2009).

Furthermore, a security problem is defined as any action that has a negative effect on the

reliability or availability of IS (Liu & Cheng, 2009). Security concerns involving the

potential theft of financial information or misuse of information may influence a

consumer’s decision on whether to conduct online transactions (Anderson & Agarwal,

2010; Coker et al., 2011; Furnell, 2010). These concerns also influence their propensity

to trust an online merchant when conducting business-to-consumer transactions (Cheung

& Lee, 2001).

Chellappa and Pavlou’s (2002) study showed a significant correlation between

consumers’ perceptions of security and their trust in electronic commerce. In a Cyber

Security Industry Alliance survey, the results showed that 50% of the participants

expressed concerns about their financial information being safe in an online environment

(Bosworth, 2006). In previous studies, 95% of the participants expressed concern about

identity theft (Nah & Davis, 2002). The Cyber Security Industry Alliance survey also

indicated an increased consumer wariness that costs online businesses billions in lost

revenue annually (Bosworth, 2006). For instance, consumer resistance to shopping

online had cost businesses $3.8 billion in lost transactions annually and the losses have

steadily increased (Bosworth, 2006).

In a study regarding the trustworthiness of an online merchant’s site (Brengman &

Karimov, 2012; Cheung & Lee, 2006; Wang et al., 2009) and customer loyalty (Coker et

al., 2011), Flavian and Guinaliu (2006) developed a structural model to investigate the

variables of trust, security, and its influence toward customer loyalty. For this research,

the data were collected through a web survey and 400 responses were collected. The key

findings of this study indicated there was a relationship between trust in Internet

shopping and consumer loyalty. The security measures perceived by online consumers

negatively influenced their propensity to trust an online merchant when shopping on the

Internet (Brengman & Karimov, 2012; Cheung & Lee, 2006; Flavian & Guinaliu, 2006).

Conversely, a limitation of perceived security is the counterproductive aspect to the

benefits of electronic commerce because of the security risks that tend to undermine an

online merchant’s loyal customer base (Coker et al., 2011; Paden & Stell, 2010). In

addition, it is important to highlight additional limitations of past research to understand

how other researchers have viewed perceived security.

The limitations of perceived security include a deficiency of innovation in the

electronic commerce structure, a lack of understanding involving the technical features of

electronic payment systems (Chellappa & Pavlou, 2002; Fakhraddin et al., 2012), and the

unresponsiveness of the payment industry to implement security features (Heikkinen &

Livarinen, 2011; Huff et al., 2010). During the beginning stages of business computing

in the early 1960s, system access was gained with a simple user password. Although

innovation has enhanced IT significantly over the past 50 years, the principle security

features used in the digital environment have remained fundamentally unchanged

(Coetzee, 2013). The surge in security vulnerabilities is difficult for organizations to

mitigate (Liu & Cheng, 2009) because the use of electronic commerce portals creates an

opportunity for criminals to take advantage of an open and vulnerable system (i.e., the

Internet; Heikkinen & Livarinen, 2011; Lee & Chen, 2010). Numerous electronic

commerce failures can be attributed to the merchant’s underestimation of perceived

security risks involved with online purchase intentions (Carter et al., 2011; Coker et al.,

2011). Organizations have attempted to address these concerns through technological

mechanisms such as authentication, encryption, and verification (Kumar & Raheja, 2012;

Laudon & Traver, 2011). These features are important because merchants must

continually assess the efficiency of their security procedures and create a safe and secure

process that can be trusted by consumers (Brengman & Karimov, 2012).

According to Fakhraddin et al. (2012), previous empirical studies do not clearly

reveal the consumer’s conception of the technical features of electronic payment security.

Although online businesses and electronic payment providers have implemented specific

security measures (i.e., security enhancing technology, such as chip and PINs; Heikkinen

& Livarinen, 2011; Merschen, 2010), this area is still lacking relative to the perceived

trust and security towards the use of this technology (Carter et al., 2011; Fakhraddin et

al., 2012). This finding is consistent with Chellappa and Pavlou’s (2002) conclusion that

many online shoppers are not familiar with the technical details of electronic payment

systems. Usually, consumers evaluate the security of an online merchant based on his or

her experience during the user interface (Wang, Minor, & Wei, 2011). For this reason,

online merchants must attract electronic payment system users, enhance their

understanding of security features, and maintain their trust (Boritz & No, 2011;

Chellappa & Pavlou, 2002; Fakhraddin et al., 2012).

Online transaction security is a technical component of an online checkout

process and includes the guarantee on behalf of the merchant to protect consumer

information in accordance with legal requirements and ethical practices (Boritz & No,

2011; Scarle et al., 2012). From a technical viewpoint, transaction security is essential

for consumer confidence toward online shopping. This confidence depends on the online

retailer’s ability to improve availability, integrity, and privacy (Brengman & Karimov,

2012; Guynes et al., 2011). System availability means all required components exist to

support consumer electronic transmission requirements. Integrity indicates the messages

sent and received electronically are not altered. Privacy means only the intended

recipient views the messages containing the transmitted sensitive information (Boritz &

No, 2011; Guynes et al., 2011).

Another problem is that an online merchant’s enterprise security network tends to

be reactive and “creates complicated interactions between protocols and systems that can

cause incorrect behavior and slow response to attacks” (Guynes et al., 2011, p. 1).

Heikkinen and Livarinen (2011) concluded it was not a lack of standards and knowledge

on behalf of the payment industry, but what the industry lacked was a commitment to

implementing them with the customer in mind. Electronic commerce security experts

suggested two strategies to ensure the network security of online businesses is monitored

and enhanced with the latest countermeasures. The first strategy involves protecting the

network integrity of the online merchant. The second strategy ensures security and

privacy between the consumer and the online company (Guynes et al., 2011). Online

vendors must invest a considerable amount of resources (i.e., financial, time, and effort)

to establish and maintain their websites (Ahrholdt, 2011). The challenges of

implementing an electronic payment system include security issues, such as fraud,

privacy, and technical problems (Eun-Jung & Park, 2010; Raja et al., 2008; Salmony,

2011). For instance, Merschen (2010) found the concurrent use of magnetic stripe cards

and chip and PIN cards mean that two different payment instruments must coexist for

customer convenience. In this context, chip and PIN instruments must still bear the

traditional magnetic stripe for wider acceptance. Furthermore, point of sale terminals

must be designed to accept the traditional magnetic cards (Merschen, 2010). As a result,

the enhanced security of the chip and PIN are just as vulnerable because they are

combined with a less secure magnetic stripe (Merschen, 2010; Sumanjeet, 2009).

Electronic commerce transactions are vulnerable to security problems and threats

(Boritz & No, 2011; Zhao & Zhao, 2012). Vulnerabilities identified in IS have increased

at a rapid pace (Furnell, 2010). Addressing known vulnerabilities in the electronic

payment process causes fraudsters to look for other means to exploit the networks

(Brengman & Karimov, 2012; Coetzee, 2013). Accordingly, Menn, a cyber-security

expert, is credited with stating, “Cybercrime is very bad news for the economy overall if

people lose faith in electronic commerce” (Coetzee, 2013, p. 77). To enhance the user

experience in terms of trust and safety during the electronic payment system process,

online businesses must implement security features (Boritz & No, 2011; Heikkinen &

Livarinen, 2011). Online business owners must also reduce the risk of security

vulnerabilities to fraud and data breaches (Cheney et al., 2012; Furnell, 2010).

In the year 2007, Internet fraud cost businesses within the United States $7.1

billion in losses (Chong, 2010). One example of a significant security data breach

occurred at TJX Inc. (Gates & Jacob, 2009; Zhao & Zhao, 2012). In this 2007 data

breach, hackers stole 455,000 records containing the names and driver’s license numbers

of nearly 46 million cardholder accounts (Gates & Jacob, 2009). This data breach

affected 48 million additional consumers and cost TJX $130 million to settle consumer

claims (Gates & Jacob, 2009). Furthermore, occurrences of credit card fraud are not

under control, which was evident by the large-scale breaches of other organizations, such

as Amazon.com and Buy.com (Giles, 2011). In addition, T.J. Maxx and Heartland

reported that credit card information from several million users had been compromised

(Merschen, 2010; Zhao & Zhao, 2012). Not only did this compromise cause an increase

in costs related to recovery efforts, customer and merchant compensation, and services,

but this also resulted in a reduced level of consumer confidence regarding the security

and integrity of the payment systems (Eun-Jung & Park, 2010; Merschen, 2010). Yet, in

another example, data containing information for 360,000 consumers were stolen in May

2011 from Citigroup. Within weeks, Citigroup reported that nearly $3 million in

fraudulent payments had already occurred on 3,400 customer cards (Coetzee, 2013).

Perceived security is an essential feature of electronic commerce and its payment

systems (Cheney et al., 2012). Online merchants must protect the details of consumer

information from internal and external fraud and criminal activity (Carter et al., 2011;

Ozkan et al., 2010). The significance of perceived security is related to the current study

in terms of the vulnerabilities involved with electronic commerce transactions and its

payment systems (Gates & Jacob, 2009; Liu & Cheng, 2009; Ozkan et al., 2010).

Security problems involving threats, such as attacks and exploitations, may result in data

breaches, stolen financial information, and identity theft (Coker et al., 2011; Guynes et

al., 2011; Liu & Cheng, 2009). For this reason, online merchants must adopt a security

strategy to instill consumer confidence and improve an online merchant’s electronic

commerce application (Carter et al., 2011; Kovacs et al., 2011; Simon, 2011). To help

alleviate the concerns of perceived security involving consumer confidence (Simon,

2011), online merchants consult with third-party organizations, such as the Better

Business Bureau (BBB), HackerSafe, and VeriSign (Sinclair et al., 2010; Warrick &

Stinson, 2009).

Recognition of Third Party Existence

Third party organizations are used to provide assurance, authentication, and

reputation to increase an online vendor’s integrity through the implementation of privacy

seals, security symbols, and vulnerability symbols (Sinclair et al., 2010; Warrick &

Stinson, 2009). Privacy seals are used to certify an online merchant’s consumer data

collection and usage with tools provided by organizations such as TrustE and BBB.

Security symbols assure the consumer that the website has the capability to use SSL

(Boritz & No, 2011; Coetzee, 2013, MacEwan, 2013; Merschen, 2010; Salmony, 2011)

and cryptographic protocols to protect their private and financial information with

applications, such as GeoTrust and VeriSign (Sinclair et al., 2010; Warrick & Stinson,

2009). Vulnerability symbols mean third-party organizations scan a merchant’s website

for vulnerabilities with tools such as HackerSafe and WebAssured (Sinclair et al., 2010).

Third party recognition is supported through web assurance, which is a symbol

used to identify businesses with the required e-commerce standards. For instance, the

VeriSign seal is commonly used to identify assurances that a merchant’s website has

been tested for trustworthiness. VeriSign is one of the largest assurance providers and

protects over a million web servers with digital certificate technology, which includes

93% of Fortune 500 company websites (Bosworth, 2006; Furnell, 2010; Warrick &

Stinson, 2009). Other organizations providing web assurance included WebTrust,

SysTrust, and TRUSTe (Boritz & No, 2011; Warrick & Stinson, 2009). To illustrate, an

experimental study was conducted to address the problem of an increased rate of identity

theft and data breaches associated with personal data collected from consumers. The

purpose of this study was to investigate trust-building tactics and the influence it has on

consumers and online vendors (Sinclair et al., 2010). The study included factors

concerning consumer trust, information privacy, and third party organizations. Social

pressure (i.e., subjective norms) was also a factor that featured research questions

involving a consumer’s willingness to provide their personal information based on trust,

institutional trust, and social influences (Sinclair et al., 2010; see also Cheung & Lee,

2006; Hoehle et al., 2012; Lee et al.., 2011; Sun, 2010b; Vachon, 2011; Yaghoubi et al.,

2011).

Specifically, a quasi-experiment in the form of a 3x3x3 between-subjects design

was applied to test the hypotheses (Sinclair et al., 2010). A survey method was employed

for this study and 628 responses were collected from college students. The experiment

involved treatment groups to determine the usage of an unknown Internet business that

offered a preferred product at a reasonable price. The threat to validity involved the one-

way method of measuring the variables with the treatment effect of the hypothetical

online business that did not fully represent institutional trust and social influence. A two-

way design was performed to improve this limitation. The results of the study indicated

significant effects and substantiated the research questions. The study presented a

practical prediction model for online merchants to implement trust-building mechanisms

for consumers through third party organizations (Sinclair et al., 2010; see also Nigriny &

Sabett, 2010).

Both consumers and merchants face risks during Internet transactions (Brengman

& Karimov, 2012; Coker et al., 2011; Heikkinen & Livarinen, 2011; Huff et al., 2010;

Fakhraddin et al., 2012 Lee & Chen, 2010; Paden & Stell, 2010; Rajamma et al., 2009;

Warrick & Stinson, 2009). Warrick and Stinson (2009) addressed the problems related to

the exponential growth of online sales and the significant increase of personal

information submitted during Internet sales. The purpose of the study was to examine

consumer perceptions of online shopping and to provide recommendations concerning

web assurance and Internet insurance to enhance consumer online activities. The authors

expanded previous research to examine consumer perceptions toward online purchasing.

When Internet sales increase, so does the volume of personal information used during the

exchange. Personal information included credit card details as well as banking

information. Two factors regarding consumer intention and consumer confidence were

tested using a triangulation method. Responses were obtained from 57 participants

attending a regional university. A within-subject design was then applied to measure the

perception of Internet purchasing. Their study showed how web assurance and Internet

liability could minimize consumer risks for effective online commerce through consumer

recognition of third-party existence (Warrick & Stinson, 2009; see also Nigriny & Sabett,

2010; Sinclair et al., 2010). The key findings in the study indicated the importance of

customer confidence for increased growth in electronic commerce (Warrick & Stinson,

2009).

Most notably, consumer’s online purchase intentions were found to increase

through online merchants with web assurance or web insurance (Hu, Wu, Wu, & Zhang,

2010). Therefore, online merchants should develop strong controls of electronic

commerce IS to support the use of third-party organizations (Warrick & Stinson, 2009).

Researchers extended the application by adding to the current body of knowledge for

practical applications to IS research (McKnight et al., 2011; Sun, 2010a; Warrick &

Stinson, 2009). The limitation of Warrick and Stinson’s (2009) study was the small

population size that might affect the validity of the results; however, the authors selected

a nonparametric test that was appropriate for the small sample size. The study showed an

increase of consumer purchase intentions with businesses incorporating web assurance

and web insurance resulting in electronic commerce growth (Warrick & Stinson, 2009).

In addition to Warrick and Stinson’s study, Fisher and Chu (2009) expanded their

research to study the influence of third party seals from a domestic and international

perspective.

Accordingly, Fisher and Chu’s (2009) study was conducted to determine if

domestic and international locations and web assurance seals influenced consumer’s

trusting beliefs of an online merchant’s website. Six web treatments were introduced to

181 participants with domestic and international website vendors and three types of web

assurance seals (i.e., no web seal, TRUSTe seal, and WebTrust seal). The data were

analyzed using ANCOVA and multiple regression analysis. The results indicated that

geographical location influenced initial trust formation. However, it was found that web

assurance seals had little effect on consumer trusting beliefs (Fisher & Chu, 2009). The

findings with web assurance seals were in contradiction to the study conducted by

Warrick and Stinson (2009), which indicated consumer purchase intentions increased

with businesses that incorporated web assurance seals. Nevertheless, research has shown

that consumers are more willing to conduct business with websites that use qualified third

party organizations to audit and approve the practices of a website (Nigriny & Sabett,

2010). Merchants can improve consumer behavior towards security perceptions (Coker

et al., 2011; Guynes et al., 2011; Simon, 2011) by collaborating with third party

organizations, such as VeriSign (Nigriny & Sabett, 2010; Sinclair et al., 2010; Warrick &

Stinson, 2009), to instill consumer confidence in an online retailer (Blockley &

McDowell, 2010). The recognition of third party existence is a key predictor whereas

organizations provide necessary assurances to engender trust between online merchants

and consumers (Sinclair et al., 2010). Third party assurances were found to be beneficial

for consumers when conducting online transactions through an electronic payment

system (Ozkan et al., 2010; Warrick & Stinson, 2009).

Electronic Payment Systems

During the mid-1990s, new payment technologies were introduced to target

business-to-consumer transactions over the Internet (Cheney et al., 2012; Leibbrandt,

2010; Mackman & Sanders, 2010; Williams & Moons, 2012). An electronic payment

system is a web-based application on a merchant’s site used to facilitate the process

between the customer and merchants for the purchase of products and services

(Fakhraddin et al., 2012; Khoshnampour & Nosrat, 2011; Teitelbaum & Lamberg, 2010).

The impetus for developing electronic payment systems was to provide a convenient,

efficient, and secure method for the banking industry and customers to exchange money

(He & Mykytyn, 2007; Heikkinen & Livarinen, 2011; Hewitt, 2011; Knorr, 2009; Ozkan

et al., 2010; Salmony, 2011; Tsarenko & Tojib, 2009). Business-to-consumer electronic

commerce is dependent on an effective electronic payment system (Blockley &

McDowell, 2010; Cheney et al., 2012; Leko et al., 2013; Sumanjeet, 2009), which is the

foundation of a successful online business operation (Fakhraddin et al., 2012; Valvi &

Fragkos, 2012).

The development of electronic commerce has been instrumental in that online

businesses have transferred traditional brick and mortar processes to electronic portals

(Ghandour et al., 2010; He & Mykytyn, 2007; Valvi & Fragkos, 2012), which have

become a common business strategy (Kumar & Raheja, 2012; Valacich, 2012; Yang, Lu

& Chau, 2012). In the United States, over 80% of retailers utilize brick and mortar and

online channels to sell their merchandise (i.e., Wal-Mart, Toys R Us, and Old Navy)

(Oudan, 2011; Ratchford & Barnhart, 2011; Yang et al., 2012). Electronic payment

systems are convenient and provide flexibility for consumers and merchants, making

them popular for business-to-consumer transactions (Cheney et al., 2012; Hannah &

Lybecker, 2010; Leko et al., 2013; Park & Gretzel, 2010; Salmony, 2011).

Although researchers have indicated a rapid growth of electronic commerce

(Chen & Sharm, 2011; Sun, 2010a; Taddeo, 2009), the growth of electronic technologies

has resulted in the need for advanced technical support for an online merchant’s payment

system (Mackman & Sanders, 2010; Ozkan et al., 2010; Raja et al., 2008; Ratchford &

Barnhart, 2011). For instance, recent statistics of online retail sales in the United States

have shown that sales are expected to reach $248 billion by 2014 (Chen & Sharma, 2011)

and are expected to grow 11% annually (Ahrholdt, 2011). Regardless of the optimistic

forecasts, the adoption rates of electronic commerce technology have slowed because of

the limitations involving the lack of consumer confidence (Moshref Javadi et al., 2012;

Ratchford & Barnhart, 2011). The lack of consumer trust (Kord et al., 2011; Wang et al.,

2009; Yaghoubi et al., 2011), perceived privacy (Mothersbaugh et al., 2012; Nicoleta et

al., 2010; Rajamma et al., 2009), and perceived security (Coker et al., 2011; Guynes et

al., 2011; Simon, 2011) negatively affects consumer trust and confidence in the electronic

payment channels and their purchase intentions (Brengman & Karimov, 2012; Ling et al.,

2010; Moshref Javadi et al., 2012).

Business-to-consumer electronic commerce involves various payment instruments

offered by online merchants such as electronic cash, debit and credit cards, and electronic

checks (Cheney et al., 2012; Fakhraddin et al., 2012; Leko et al., 2013; Ozkan et al.,

2010; Sumanjeet, 2009). Regardless of the payment instrument used by consumers, the

most cited problem with electronic payment systems was the perceived insecurity and

lack of trust in the payment system or the payment instrument (i.e., credit card;

Heikkinen & Livarinen, 2011; Merschen, 2010; Paden & Stell, 2010; Salmony, 2011).

Despite the investments that organizations have placed in their electronic payment

systems (i.e., credit and debit cards, electronic banking, and automated clearing house

[ACH]), numerous studies have shown that paper-based payment instruments (i.e.,

checks) are still widely used in the United States (Coven, 2010; Gorsline & Hosie, 2009;

Leibbrandt, 2010; Salmony, 2011).

In the year 2008, consumers provided 26 billion checks to pay for products

(Leibbrandt, 2010). Coven (2009) compared the costs associated with payment by credit

cards, ACH, and checks. For example, when consumers pay by credit card, the

transaction cost is essentially free. Consumers using ACH pay less than $ .15 per

transaction, and the use of a paper-based check costs approximately $1.25 for each

transaction (Coven, 2009). Likewise, Salmony (2011) stated that approximately $11

billion in check payments were produced in 2009 and $2 billion were processed through

ACH transactions. It is well understood in the payment industry that for business-to-

consumer retail transactions, electronic payments are faster and cheaper than traditional

paper-based instruments (i.e., checks), which is not any better than cash (Salmony, 2011).

Online merchant’s electronic payment systems are dependent upon existing

technologies that allow merchants to provide payment services that use information and

communication technologies (Knorr, 2009; Raja et al., 2008). This is also influenced by

economic needs that has resulted in various payment systems for both high and low value

transactions (Knorr, 2009). In the United States, the majority of the retail payments

completed by consumers involve low value transactions settled by cash and check

instruments (Knorr, 2009). For example, cash instruments are one of the most expensive

methods because of the increased cost associated with storing, distributing, and securing

cash. Likewise, checks are much more expensive than electronic payment methods

(Heikkinen & Livarinen, 2011).

An electronic commerce system requires an effective payment system in order to

provide efficient transactions in terms of disbursement and automated collection

functions (Coven, 2010; Park & Gretzel, 2010). By relying on traditional cash and check

instruments, the payment systems are not standard across the industry. Different

standards have been developed that are inefficient and too costly to maintain in the

current economy. This is evident because current payment systems were built on

traditional cash and account-based methods (Fakhraddin et al., 2012) that are still being

used to accommodate various consumers’ choice of payment (Ismail, 2013; Knorr, 2009).

However, if a company’s primary method of collecting payment is by check then they are

paying more than they would by collecting payments electronically (Coven, 2010). In

commerce, the point is to sell and purchase products and services, and the electronic

payment process should not be a barrier to that objective (Brengman & Karimov, 2012;

Ebben, 2013).

Electronic commerce companies use merchant and bank accounts to process and

receive electronic payments through online transactions (Teitelbaum & Lamberg, 2010).

To explain this process, when consumers wish to make an online purchase, the items

must be added to the sites’ shopping cart (Kukar-Kinney & Close, 2010; Park & Wang,

2013; Vachon, 2011). The customer sends the appropriate financial information to the

merchant through a secure site using a method of encryption known as SSL by entering

the appropriate information into the online order form (Carter et al., 2011; Kumar &

Raheja, 2012; Sinclair et al., 2010). An important feature of SSL is it does not

authenticate an individual or an online merchant (Coetzee, 2013, Merschen, 2010;

Salmony, 2011). The customer must then complete the web order form with his or her

name, credit card number, the expiration date, and the CVV2/CVC2/CVA2, which is the

3-digit code on the reverse side of the credit card (Mangiaracina & Perego, 2009). The

merchant payment system is used to authenticate and verify the account through a

clearinghouse. For example, the ACH system was developed for businesses to make

payments among each other and to collect payment from its customers. However, based

on the existing technology to support business-to-consumer electronic payments, the use

of the ACH has rapidly grown (Coven, 2009; Knorr, 2009; Leibbrandt, 2010). Once the

transaction is approved, the customer’s account is debited, and the online merchant’s

account is credited for the sale. The customer then receives details of the transaction

electronically and in the regular banking statement (Kumar & Raheja, 2012; Laudon &

Traver, 2011; Leko et al., 2013).

Salmony (2011) stated that the success of electronic payment transactions may be

enhanced if the process is transparent to the consumer (Salmony, 2011). The

transparency of the payment method to the user positively influenced online shopper’s

experience (Ebben, 2013). For example, compared to the model used by the majority of

the banking industries, Apple’s iTunes was successful in deploying a transparent model

(Salmony, 2011). Amazon’s payment model also provides for transparent payment

services (Vanetti, 2010). In Ebben’s (2013) study, the transparency or hidden payment

was correlated with increased consumer spending. To clarify how transparency relates to

payment instruments, cash was found to have a high transparency rate, checks had a

medium transparency, credit cards had a low transparency rate, and pre-paid cards were

considered the lowest. For this reason, online merchants prefer to have their customers

use a payment instrument with a low transparency rate (Ebben, 2013).

Electronic payment system services contain IT features that provide specific

functions during the payment process (Farrow, 2013; Sun, 2010). These functions may

be categorized as front end, back end, or shared processing. First, gateways and customer

channels are a front-end process that provides consumers the ability to interact with the

merchant directly. An example of a front-end component is account validation that refers

to the authentication of a payee’s account in the payment instruction. Second, product

systems and payment engines are categorized as a back end function. For instance, funds

control is an example of a back end component that is used to verify whether sufficient

funds are available to process a payment. Third, a shared function is routing that

indicates the selection and transmission of payments to a certain destination (Farrow,

2013; Kumar & Raheja, 2012).

Mangiaracina and Prego (2009) examined various electronic payment systems to

determine whether they pose barriers to online consumers conducting business-to-

consumer transactions. The methodology was comprised of two case studies based on

multiple descriptive variables (Vendor CEOs or Marketing managers) and multiple

explanatory variables (Visa and MasterCard issuers), with the latter focusing on online

payment security from a provider perspective. An overview of suitability issues

concerning online payment systems was discussed. Mangiaracina and Prego contradicted

other studies by downgrading the significance of consumer trust, which other authors had

found to be a determinant for the use of electronic payment systems (Blockley &

McDowell, 2010; Heikkinen & Livarinen, 2011). Mangiaracina and Prego summarized

their research and stated, “The main driver affecting the diffusion of the different

payment systems is their suitability to the online channel and not the trust of users” (para.

5). Ozkan et al. (2010) found that the lack of fit for purpose (i.e., suitability) was the

most noted reason why electronic payment systems may be hindered. For this reason,

value must be created in the retail the payment system through innovation. In this

context, innovation is defined as “creating value by doing things differently, with Value =

Benefits/Costs” (Blockley & McDowell, 2010, p. 28). Therefore, the suitability of the

payment systems is essential for various stakeholders, such as consumers, merchants,

creators, and regulators of the payment system (Blockley & McDowell, 2010).

In a 2009 Javelin Strategy and Research study, the results showed that online

shoppers use of credit cards for business-to-consumer transactions are falling rapidly. It

was predicted that one-third of these transactions would be completed through alternative

payment processes by 2013, bypassing traditional banking and online merchant

arrangements (Blockley & McDowell, 2010). Consumer perceived security concerns are

one of the primary reasons why consumers are using alternative payment methods

(Blockley & McDowell, 2010), and to fill the gaps in services (i.e., speed, convenience,

and customer loyalty; Blockley & McDowell, 2010; Vanetti, 2010). Moreover, in a

Cisco survey of 1,500 participants, it was found that alternative payment systems are

becoming more popular as over 35% of the participants reported “frequent” and “very

frequent” use of alternative third party organizations, such as PayPal, Bill Me Later,

Amazon Checkout, and Google Checkout (Blockley & McDowell, 2010;Milkau, 2010).

These organizations provide flexible payment service for consumers (Vanetti, 2010). For

example, PayPal utilizes existing financial infrastructure of bank accounts and credit

cards to create a global, real-time payment service. PayPal has obtained 150 million

members globally in over 56 countries (Blockley & McDowell, 2010; Leibbrandt, 2010;

Salmony, 2011).

Amazon has also provided its own payment solutions with its billing and account

management that can be used by online consumers to purchase applications (Vanetti,

2010). Google Checkout is another platform that is competing with existing electronic

payment systems by providing third party payment solutions for consumers (Milkau,

2010). These alternative payment platforms use the interaction of the social

communities, but they are not the Internet markets or banks. The banking industry is the

foundation for online payment services in the modern world (Milkau, 2010). As a

solution, traditional retailers may look to alternative payment platforms and evolve

towards a real time economy to respond to customer and market needs (Hewitt, 2011;

Vanetti, 2010).

Millions of consumers and businesses in the United States continue to use cash

and checks because of its convenience and accessibility. They will continue to use these

payment instruments until the market provides a good reason and means to adopt a more

efficient electronic payment method (Hewitt, 2011). Consumers’ perceived risk, security,

and costs are the driving factors when selecting a payment instrument (Heikkinen &

Livarinen, 2011). In spite of the efforts to increase consumer confidence, between 2006

and 2008, online businesses attempted to drive consumers to use electronic payment

systems. They predicted an increase from 39% to 55%, but failed to reach this goal as

the use of electronic payment systems only reached 41%, which was a 2% increase

(Coven, 2010; Hannah & Lybecker, 2010; Park & Gretzel, 2010). Consumers do not

want a payment instrument that is not accepted everywhere (Heikkinen & Livarinen,

2011). Salmony (2011) stated, “Trying to wrest the stakeholders (especially consumers)

away from an instrument to which they are so very attached for very objective and habit

forming subjective reasons is probably the most difficult course” (p. 259). Previously,

online customers did not have a direct influence in terms of payment methods. Blockley

and McDowell (2010) found consumer acceptance of electronic payment systems and its

payment instruments are guided by the six Cs: (a) capability (i.e., adding value to the

payment channels or supply chains), (b) cost (i.e., driver of innovation or change), (c)

convenience (i.e., consumer’s expectation of speed and real time transactions), (d)

coverage (i.e., access to the Internet is correlated with increased use of electronic

commerce), (e) confidence (i.e., payment systems meets consumer expectations), and (f)

confidentiality (i.e., consumer’s expectation that a payment system is secure and

trustworthy).

He and Mykytyn (2007) developed a framework to understand the factors that

influenced consumer acceptance and adoption of online payment systems. A technology

acceptance model was developed that included the constructs of perceived usefulness and

ease of use as significant factors. The technology acceptance model is an extension of

TRA, which was used to understand and predict consumer electronic commerce adoption

(Aboelmaged, 2010; Yousafzai et al., 2010). In the study, 148 participants were surveyed

to determine the relationships between perceived risk, perceived benefits, merchant

system features, and intention to use an online payment system (He & Mykytyn, 2007).

The results indicated perceived trust and risk are key factors that influence consumer’s

behavior to use or not use online payment systems (He & Mykytyn, 2007; see also

Blockley & McDowell, 2010; Heikkinen & Livarinen, 2011; Merschen, 2010; Salmony,

2011). The limitations of this study were described with recommendations for a more

diverse sample by geographic region and age (He & Mykytyn, 2007). Recommendations

were made for future research, which called for a multi-item construct as a more

appropriate means when investigating online consumer intentions (Diamantopoulos et al.,

2012; He & Mykytyn, 2007).

Ozkan et al. (2010) addressed the lack of consumer adoption of electronic

payment systems by identifying the critical factors for a successful electronic payment

system from the consumer’s perspective. A secondary goal of the study was to address

consumer adoption of electronic payment systems as a source for facilitating online

transactions. The study involved utilization of a survey design method. A web-based,

self-administered questionnaire was created using a 5-point Likert scale consisting of 12

questions that quantified risk, trust, security, and web assurance seals. The survey was

distributed to participants via email and 155 respondents returned the survey for a 77.5%

response rate. The responses indicated that consumers fear their personal information is

not safe in an online environment. The key findings revealed consumer concerns with

security, trust, and assurances were factors for successful electronic payment adoption.

These consumer concerns indicated that improvements in the electronic payment system

are still required to enhance trust in an online merchant’s payment system (Ozkan et al.,

2010). Likewise, Blockley and McDowell (2010) also found improvements are

necessary to fill the gap in the market in response to both consumer and online vendor’s

needs. The electronic payment industry is an innovative and ever changing environment

(Ismail, 2013). Previously, retailers have initiated changes through innovation that were

not readily accepted by consumers or merchants. For this reason, electronic system

designers working within business-to-consumer online environments must provide value

added benefits to the merchant and customer by improving the electronic payment

systems (Ozkan et al., 2010). As a solution, implementing innovative changes to the

traditional payment systems may require a modest, but continuous process. Therefore, all

stakeholders may accept a new innovative process if it positively influences the behavior

of online shoppers and merchants alike (Blockley & McDowell, 2010).

A study of business-to-consumer electronic commerce was guided by the

innovations adoption model) to analyze electronic commerce adoption (Crespo &

Rodriguez, 2008). The theoretical foundation for the innovations adoption model

included constructs of TRA, such as attitudes and subjective norms (i.e., influence from

others; Crespo & Rodriquez, 2008; see also Awa et al., 2010; Cha, 2011; Corno, 2011).

To test the model, a mixed-method approach was used, starting with a qualitative

methodology consisting of in-depth interviews with Internet experts (Crespo &

Rodriguez, 2008). A quantitative methodology was applied to examine online consumer

attitudes towards electronic commerce adoption. Consistent with other studies (Awa et

al., 2010; Cha, 2011; Corno, 2011), the findings showed consumer attitudes toward

electronic commerce and subjective norms were the primary factors determining their

intention to adopt electronic commerce technology. The authors called for further study

to address the limitation of the objectivity versus subjectivity to analyze the consistency

between consumer behavior and real purchasing intention. Specifically, Crespo and

Rodriguez’s (2008) survey instrument was selected for the current study because its

context was specific to the influence of subjective norms on a consumer’s Internet

purchase intention. Furthermore, examining subjective norms may provide a better

understanding of consumer behavior towards the adoption of electronic commerce (Awa

et al., 2010; Cha, 2011; Corno, 2011: Hoehle et al., 2012; Lee & Chen, 2010; Yousafzai

et al., 2010).

Subjective Norms

With the rapid growth of electronic business, social influence towards online

consumer behavior has been a primary topic of interest for researchers in the field of

electronic commerce (Lee & Chen, 2010; Moshref Javadi et al., 2012). However, there is

limited knowledge regarding this behavior because it is shaped by a complicated socio-

technical arrangement based on numerous factors and disciplines (i.e., marketing, IS, and

sociology; Junco, 2011; Kshetri, 2010; Lee & Chen, 2010). Previous authors have

indicated a shift in the focus from technological research (Al-Majali, 2011; Alsajjan, &

Dennis, 2010) with a greater emphasis on consumer behavior and social influence (Awa

et al., 2010; Li & Karahanna, 2012; Vachon, 2011; Yousafzai et al., 2010). Incorporating

social variables, such as subjective norms, can be measured with TRA (Aboelmaged,

2010; Al-Majali, 2011; Awa et al., 2010; Cha, 2011; Corno, 2011; Gao & Wu, 2010;

Kshetri, 2010; Lee & Chen, 2010; Moshref Javadi et al., 2012; Peslak et al., 2010;

Richetin et al., 2008; Yousafzai et al., 2010). Derived from the field of social

psychology, TRA was developed by Fishbein and Ajzen (1975) and includes two

determinants that include one’s attitude towards a behavior and the notion of social

demands (i.e., subjective norms; Al-Majali, 2011; Crespo & Rodriguez, 2008; Hoehle et

al., 2012; Sinclair et al., 2010; Yousafzai et al., 2010). Positive social influence

correlates to improved consumer behavior, attitudes, and intention to shop online (Hoehle

et al., 2012; Lee et al., 2011). Greater details are provided in a subsequent section to

address those studies that were guided by TRA.

In the context of electronic commerce adoption, subjective norms represent an

individual’s normative belief as a motivation to perform a behavior according to the

opinion of others, leading to the probability that individual’s will approve or disapprove a

certain behavior (Crespo & Rodriguez, 2008; Moshref Javadi et al., 2012; Lee et al.,

2011; Yousafzai et al., 2010). Subjective norms have been conceptualized into three

dimensions (i.e., family, friends, and media) and are considered to have a significant

effect on one’s intention to accept or adopt electronic commerce technology (Al-Majali,

2011; Bleakley & Hennessy, 2012; Crespo & Rodriguez, 2008; Sinclair et al., 2010).

Various authors have indicated support for the use of TRA to examine consumer

behavior and social influence when predicting the adoption of IS, marketing (Lee &

Chen, 2010; Vachon, 2011), and electronic commerce technology (Li & Karahanna,

2012; Yousafzai et al., 2010). In earlier studies, some authors (Shih & Fang, 2004; Shim,

Eastlick, Lotz, & Warrington, 2001) found that subjective norms had no significant

influence on an individual’s intention to use electronic commerce technology (Alsajjan &

Dennis, 2010). This may be attributed to the expansion of electronic commerce

technology (Lee & Chen, 2010) and social media networks (Brengman & Karimov, 2012;

Hannah & Lybecker, 2010; Junco, 2011; Lenhart, Purcell, Smith, & Zickuhr, 2010; Smith

& Caruso, 2010). Researchers have found that subjective norm is a determinant of one’s

intention of self-reported IT use, electronic procurement adoption (Yousafzai et al.,

2010), adoption of Internet banking in developing countries (Al-Majali, 2011; Yousafzai

et al., 2010), the use of instant messaging (IM; Peslak et al., 2010), and Internet

purchasing intention (Crespo & Rodriguez; 2008; Hoehle et al., 2012; Ozkan et al.,

2010).

Electronic commerce infrastructures are centered on social and cultural constructs

(Kshetri, 2010; Van Slyke, Lou, Belanger, & Sridhar, 2010). For example, empirical

research has shown that socio-culture is a valuable component of information ecology

that affects electronic commerce adoption (Kshetri, 2010; Moshref Javadi et al., 2012;

Taddeo, 2009). To this extent, Taddeo’s (2009) study (i.e., analysis of trust) served as a

foundation for a better understanding of the relationship between online consumer trust

and its social mechanisms (i.e., moral and social norms) that can be perceived differently

depending on one’s culture (Lee et al., 2011; Li & Karahanna, 2012; Taddeo, 2009; Van

Slyke et al., 2010).

Cultural barriers in business-to-consumer electronic commerce include gaps in

access (i.e., haves and have not’s) and the lack of face-to-face social interaction

(Brengman & Karimov, 2012; Moshref Javadi et al., 2012; Peterson & Howard, 2012).

However, as the number of Internet users increased on a global scale, the virtual

community has developed shared norms in order to regulate the behavior of Internet users

regardless of one’s culture (Taddeo, 2009; Vachon, 2011). In this context, the greater the

influence of subjective norms an individual experiences, the greater the behavioral

intention may be towards actual behavior (Aboelmaged, 2010). For example, a socially

supportive reference group enhanced a higher level of intention to adopt Internet banking

(Al-Majali, 2011; Vachon, 2011; Yousafzai et al., 2010).

In the electronic commerce specialization, researchers have applied theories to

determine consumer behavior in terms of adoption, use, and intention (Aboelmaged,

2010). TRA may add to the understanding of consumer behavior and the intention to

adopt an electronic payment system when conducting business-to-consumer transactions

(Awa et al., 2010; Bleakley & Hennessy, 2012; Cha, 2011; Crespo & Rodriguez, 2008;

Li & Karahanna, 2012; Moshref Javadi et al., 2012; Ozkan, et al., 2010; Yousafzai et al.,

2010).

Theory of Reasoned Action

TRA, developed by Fishbein and Ajzen (1975), is a parsimonious and intuitive

framework with the ability to predict consumer behavior (Alsajjan & Dennis, 2010; Awa

et al., 2010; Bleakley & Hennessy, 2012; Cha, 2011; Yousafzai et al., 2010). TRA

includes two determinants that include an attitude towards behavior and the notion of

social demands known as subjective norms (Alsajjan & Dennis, 2010; Awa et al., 2010;

Cha, 2011; Yousafzai et al., 2010). Subjective norms describe an individual’s normative

belief and the probability that other individuals will approve or disapprove of a certain

behavior (Lee et al., 2011; Moshref Javadi, 2012; Yousafzai et al., 2010). TRA is an

empirically valid and economical social psychology theory used to explain the various

determinants involving the adoption and use of IS technology research (Lee et al., 2011;

Yousafzai et al., 2010). Researchers have used TRA to explain consumer decision-

making by examining the relationship between consumer attitudes and behavior in

electronic commerce adoption research (Yousafzai et al., 2010; Ozkan et al., 2010).

In previous studies, the focus on Internet banking research encouraged

technological research (Al-Majali, 2011; Alsajjan, & Dennis, 2010). Currently, the

emphasis has shifted to researching consumer behavior (Awa et al., 2010; Yousafzai et

al., 2010). Empirical research has revealed that socio-culture is a valuable component of

information ecology that affects electronic commerce adoption (Kshetri, 2010). To this

extent, electronic commerce infrastructures are centered on social and cultural constructs

(Kshetri, 2010). A better understanding of these constructs may result in devising better

strategies in electronic commerce market diffusion (Kshetri, 2010).

TRA theorists purport that consumer behavior is governed by the consumer’s

intention to perform the behavior based on attitudes and subjective norms (Awa et al.,

2010; Cha, 2011). In the field of electronic commerce, researchers applied social

cognitive theories to study consumer behavior (Cheung & Lee, 2006; Kim et al., 2009).

Drawing from the social cognitive theory with self-efficacy as the primary determinant,

Kim et al. (2009) focused on how individuals in society see themselves and others in

various social settings. The social cognitive theory presents higher predictive power

because online consumers control and direct their decisions and actions (Kim et al.,

2009).

In terms of consumer control, TRA is not without limitations. For instance, TRA

cannot be used to generate an accurate prediction of intentions if individuals display low

volitional control (Alsajjan & Dennis, 2010). In this context, predicting behavior is the

strength of TRA, but it is dependent on individuals making rational decisions and

considering the results of their decisions (Alsajjan & Dennis, 2010). Thus, TRA can be

used to predict the intentions of individuals displaying rational choices regardless of

pressures from social norms (Corno, 2011; Hoehle et al., 2012). When consumer

behavior and intention is measured simultaneously, this method does not provide accurate

results to take full advantage of the predictive power of TRA (Yousafzai et al., 2010). To

overcome limitations in studies involving TRA and to ensure an accurate test, the

constructs of behavior must be measured objectively to ensure the greatest correlation

between behavior and intention (Yousafzai et al., 2010). For example, in a study of the

factors derived from empirical literature, TRA was used to develop the online shopping

acceptance model to guide research on marketing strategies to attract new customers and

retain existing ones (Wang et al., 2011; Zhou et al., 2007). In this longitudinal study,

consumer decision factors for adopting electronic commerce services were found to be

complex because of the risks involved. However, the findings indicated causality exists

between consumer intention and actual behavior (Al-Majali, 2011; Alsajjan & Dennis,

2010; Ozkan et al., 2010; Zhou et al., 2007).

In an applied study, TRA was used as the framework to predict consumer’s

intentions and behavior during the prepurchase and postpurchase phases (Kim et al.,

2009). When predicting consumer behavior, not only is the completion of the initial

online transaction important, but so is the long-term relationship between consumers and

merchants. Traditionally, consumer and business relationships involve trust in

salespeople, merchandise, and organizations, yet, on the Internet, hardware, software, and

virtual organizations replace these functions (Cheney et al., 2012; Kim et al., 2009). Kim

et al. (2009) concluded that limitations did exist in their study and recommended

investigating the constructs of trust and risk of consumer decisions that occur during

electronic commerce transactions.

Originally, theorists presented TRA in the social psychology field to explain and

understand human behavior (Al-Majali, 2011; Crespo & Rodriguez, 2008; Hoehle et al.,

2012; Sinclair et al., 2010; Yousafzai et al., 2010). Researchers have applied TRA to

address practical problems across several disciplines concerning IS and electronic

commerce (Al-Majali, 2011; Lee et al., 2011; Vachon, 2011). The following discussion

regards the manner in which researchers have applied TRA in practical applications

involving Internet banking, IM, electronic procurement in a developing countries, and

consumer trust in electronic commerce.

Internet banking. Theories used to study consumer behavior include the breadth

of theoretical and empirical research methods with traditional theories, such as

personality, behavioral, social psychology, and sociological theories that are relevant to

solve current problems (Lewicki et al., 1998). Al-Majali (2011) applied TRA to examine

and understand why individuals adopt or reject IS, such as Internet banking. Internet

banking users have increased exponentially and the banking industry has invested over

$182 million in upgrades to their Internet technology. Regardless of this effort, customer

adoption is relatively low with a 3.5% penetration rate. The reason for the reluctance of

consumer adoption of Internet banking is the high risk and lack of security measures of

the banks’ websites when compared to traditional facilities. Perceived risk and lack of

trust present a negative connotation regarding consumer attitudes toward Internet banking

service adoption. In the study, consumer attitude had a significant influence toward the

intent to adopt Internet banking systems (Al-Majali, 2011). In terms of subjective norms,

a socially supportive reference group enhances a higher level of intention to adopt

Internet banking (Al-Majali, 2011; Yousafzai et al., 2010). The results of this empirical

study supported TRA’s proposition that behavior towards intention-to-use the Internet

banking services was motivated by the individual’s attitude and subjective norm (Al-

Majali, 2011). TRA was found to be a relevant theory in its ability to predict the

adoption of Internet banking. TRA is applicable to the current study and represents the

characteristics of a good theory that can be applied to practice by observing, questioning,

and understanding human behavior and most importantly, one’s intention to the adoption

of Internet banking. The findings included important information for the banking

industry and suggestions as to how banks can create a positive customer attitude by

stimulating trust, decreasing perceived risk, and publicizing awareness of its services (Al-

Majali, 2011).

Furthermore, Alsajjan and Dennis (2010) conducted a study of Internet banking

acceptance that contradicted the findings of Al-Majali (2011). They concluded TRA was

not sufficient to explain situations where individuals possessed low volitional control

(Alsajjan & Dennis, 2010; Richetin et al., 2008). Richetin et al. (2008) found the major

limitation of their study was they measured behavioral expectations of the respondents

instead of intentions. Alsajjan and Dennis (2010) developed an Internet banking

acceptance model using the framework of the theory of planned behavior, which is an

extension of TRA. The Internet banking acceptance model was used to measure the

influence of trust and IS usefulness at the structural level (Alsajjan & Dennis, 2010). The

Internet banking acceptance model proved to be a rigorous and parsimonious model that

explained 80% of individual attitude intention toward Internet banking acceptance

(Alsajjan & Dennis, 2010). The next discussion shows how TRA can be applied to other

IS technology.

Instant messaging. The TRA framework was used to examine the adoption of

IM, which is described as an important method of business communication (Peslak et al.,

2010). A review of the literature had indicated very few studies involved IM, which is

considered an important topic for IS researchers and practitioners. The purpose of the

study was to understand IM behavior and encourage the adoption of this communication

tool. IM was preferred over other forms of communication such as email because of the

clear advantages provided through synchronous communication, collaboration, enhanced

social experience, relationship building, and as a direct marketing tool. Additional

features of IM presented involved a more personal link that is less intrusive than

telephone calls or email. Another point was the streamlined customer services offered

through IM and the costs savings incurred due to long-distance telephone calls and

frequent business travel. The results of the study indicated a significant and positive

correlation among attitude, subjective norms, and user intention. The success of IS

technology is dependent on user acceptance. The findings were utilized to confirm how

the components of TRA could be used to explain how one’s attitude toward IM was

positively related with the individual’s intent to use IM. TRA provided a strong fit to

measure the behavior, attitude toward, intention to use, and prediction of IM use. The

study of IM incorporated the four functions of theories, which are (a) descriptive (why),

(b) delimiting (what), (c) generative (developing new research), and (d) integrative

(understanding; Peslak et al., 2010). The following discussion pertains to how

researchers have applied TRA to study electronic procurement in developing countries.

Electronic procurement. TRA was also used to study the prediction of

electronic procurement in a developing country. According to Aboelmaged (2010), this

is the first study wherein the topic was examined in the United Arab Emirates. The

practical implications of the study provided both system developers and business

managers with an adoption model that could be employed to explain the significance of

electronic procurement and improve adoption of this technology. The social implications

of TRA indicate the importance of communication technologies in collaboration with

customers and supply chain partners. The traditional method of procurement involved

outdated communication mediums such as mail, telephone, and facsimile machines

(Aboelmaged, 2010). Web-based technologies are used to enhance and support the

procurement process by transforming technology to improve global business

(Aboelmaged, 2010; Fakhraddin et al., 2012). Electronic procurement has greatly

reduced the time and cost involved in the procurement process (Aboelmaged, 2010;

Coven, 2010).

Information technology in the United Arab Emirates is well developed and is

ranked 32nd globally (Aboelmaged, 2010). However, electronic transactions in this

country are progressing at a low rate. For this reason, the author tested TRA to explain

and predict information technology usage and behavior across a range of technology

initiatives and populations. Through direct observation, a field survey was the primary

method used to collect and analyze the empirical data in Aboelmaged’s (2010) study.

TRA proved to be a robust method with its explanatory power and provided strong

empirical support of predicting user intention of electronic procurement. TRA

substantiated the consumer’s intention to use this technology was determined by attitude

and influenced by subjective norms (Aboelmaged, 2010). TRA contained nomothetic

properties that were rigorously tested resulting in higher levels of predictability and

explanatory power (Gay & Weaver, 2011). The findings of Aboelmaged indicated TRA

was useful when applied to practical business problems (Corley & Gioia, 2011).

Furthermore, the results added value to the body of knowledge for professionals by

improving system development, implementation, and adoption of electronic procurement

technology in developing countries (Aboelmaged, 2010; Gao & Wu, 2011). The

following discussion is the addressing of electronic commerce relationships.

Electronic commerce relationships. TRA was applied to a study of trust and

satisfaction in electronic commerce relationships in a longitudinal study, where Kim et al.

(2009) addressed consumer trust and satisfaction as two important factors for successful

electronic commerce relationships. Consumer satisfaction is an individual attitude

shaped by the cognitive evaluation of the expectations of the quality of service or

merchandise received in the online exchange process (Kim et al., 2009). TRA was

implemented to provide the rationale to understand the relationships among attitudes,

intentions, and behavior. The theoretical and practical implications provided numerous

strengths for this study. First, Kim et al. proclaimed this was the first longitudinal study

of consumer trust and satisfaction. The stages of prepurchase and postpurchase

phenomena were studied over time to provide a comprehensive study of electronic

commerce behavior. Second, the premises of TRA went beyond the purpose of the study

and provided new knowledge for the authors to explain consumer prepurchase and

postpurchase satisfaction. Thus, consumer trust does not only affect immediate

purchasing decisions; it also affects the long-term relationships with online vendors. The

findings of the study are a contribution to the body of knowledge in electronic commerce

research and could be utilized to provide online merchants practical insight to facilitate

an environment of trust for successful long-term relationships with consumers (Kim et

al., 2009). Electronic commerce adoption theories (Wang et al., 2009) are directly related

to the factors of consumers’ propensity to trust, perceived privacy, perceived security,

subjective norms, recognition of third-party existence, and the intention to adopt an

electronic commerce payment system.

TRA is pertinent to studies of consumer behavior because its constructs have been

used to address practical problems across several disciplines concerning IS and electronic

commerce (Al-Majali, 2011; Bleakley & Hennessy, 2012; Vachon, 2011). Consumer

behavior can be predicted by one’s intention to engage in an activity (Cha, 2011;

Fishbein & Ajzen, 1975). In addition, TRA includes two determinants: attitude towards a

behavior and the influence of social pressure known as subjective norms (i.e., influence

from family, friends, and media; Fishbein & Ajzen, 1975; Yousafzai et al., 2010). TRA

is a primary theory of current interest used to examine consumer behavior, social

influence, and one’s intention to participate in electronic commerce (Corno, 2011). The

components of the TRA, specifically, behavior, social norm, and intention have been

used as the framework to examine the relationship and test the predictive strength of

predictors towards consumer intention to adopt an electronic payment system (Corno,

2011; Crespo & Rodriguez, 2008).

Summary

Online merchants have implemented various measures through third party

organizations to protect consumers with privacy policies, security measures, and web

assurance (Coker et al., 2011; Furnell, 2010; Guynes et al., 2011; Sinclair et al., 2010).

In the electronic commerce specialization, empirical research exists to address consumer

online shopping behaviors that are connected through the norms and values of social

institutions (Kshetri, 2010; Vachon, 2011). Researchers have found that consumer trust,

perceived privacy, and perceived security were major barriers in the relationships

between consumers and online merchants (Blockley & McDowell, 2010; Cheung & Lee,

2001; Coetzee, 2013; Coven, 2009; Coven, 2010; Crespo & Rodriguez, 2008; Ebben,

2013; Farrow, 2013; Fisher & Chu, 2009; Goles et al., 2009; Guynes et al., 2011;

Heikkinen & Livarinen, 2011; Hewitt, 2011; Ismail, 2013; Khoshnampour & Nosrat,

2011; Kim & Benbasat, 2003; Knorr, 2009; Kord et al., 2011; Kukar-Kinney & Close,

2010; Leibbrandt, 2010; Merschen, 2010; Milkau, 2010; Nicoleta et al., 2010; Ozkan et

al., 2010; Nah & Davis, 2002; Raja et al., 2008; Roca et al., 2009; Salmony, 2011; Sellen

& Belczyk, 2011; Simon, 2011; Sinclair et al., 2010; Teitelbaum & Lamberg, 2010;

Vanetti, 2010; Yaghoubi et al., 2011).

TRA has been documented by researchers in the field to help explain how

consumer’s attitudes and social norms influence their intention towards the acceptance of

electronic commerce technology (Aboelmaged, 2010; Al-Majali, 2011; Awa et al., 2010;

Bleakley & Hennessy, 2012; Cha, 2011; Corno, 2011; Gao & Wu, 2010; Kshetri, 2010;

Lee & Chen, 2010; Li & Karahanna, 2012; Peslak et al., 2010; Richetin et al., 2008;

Vachon, 2011; Yousafzai et al., 2010). TRA has been used to provide the framework to

gain a greater understanding of constructs under investigation (Kim et al., 2009;

Yousafzai et al., 2010; Ozkan et al., 2010), but the constructs have not been collectively

studied relative to consumer intention to adopt an electronic payment system.

The proposed study is of current interest since the rapid growth of electronic

commerce has created new opportunities for online merchants and consumers (Chen &

Sharma, 2011; Cheney et al., 2012; Leko et al., 2013; Moshref Javadi et al., 2012; Ozkan

et al., 2010; Raja et al., 2008; Salmony, 2011; Taddeo, 2009; Vachon, 2011; Valacich,

2012). Furthermore, electronic commerce is successful when consumers trust the virtual

environment (Goles et al., 2009; Heikkinen & Livarinen, 2011; Kord et al., 2011; Raja et

al., 2008). Online merchants have also implemented various measures through third

party organizations to protect consumers with privacy policies, security measures, and

web assurance (Coker et al., 2011; Furnell, 2010; Guynes et al., 2011; Sinclair et al.,

2010). An effective electronic payment system is necessary in the electronic marketplace

to respond to changes in consumer socio-economic trends, which benefits both

consumers and merchants (Fakhraddin et al., 2012; He & Mykytyn, 2007; Moshref

Javadi et al., 2012; Ozkan et al., 2010; Raja et al., 2008; Ramanathan, 2010; Salmony,

2011). The results of the conducted study will possibly be used to add valuable

knowledge to the electronic commerce literature with a greater understanding of how

TRA was applied to accomplish the goals of this study (Simon, 2011).

Chapter 3: Research Method

Presented in Chapter 3 is a description of the research procedure for this

quantitative ex post facto study. The intent of this chapter is to restate the research

problem and purpose as well as the research questions and associated hypotheses used to

guide this study. In the remaining sections that follow, an explanation of the chosen

research method and design used to fulfill the goals of this study were addressed.

Furthermore, the population and sample for this study were described and the materials

and instruments were explained. Operational definitions of the predictor variables and

the criterion variable were defined for the purpose of this research. A description of the

data collection, processing, and analysis was presented and the assumptions, limitations,

and delimitation were addressed. Finally, ethical assurances were discussed and a brief

summary of the research method concludes this section.

Statement of the Problem

In 2010, approximately 228 million adults had access to the Internet in the United

States (U.S. Census Bureau, 2012). The market for business-to-consumer electronic

commerce has expanded rapidly in the first decade of the 21st century, but is still far

from reaching its potential (Hannah & Lybecker, 2010; Leko et al., 2013; Moshref Javadi

et al., 2012; Valacich, 2012; Wu et al., 2012). Consumers’ unwillingness to trust and

participate in online transactions has cost the U.S. retail industry approximately $6.5

billion in lost sales annually (Rajamma et al., 2009). The lack of consumer confidence is

one of the reason for failure in electronic commerce (Blockley & McDowell, 2010),

limiting the adoption of its payment systems (Cheney et al., 2012; Ozkan et al., 2010),

and affecting the long-term profitability of online businesses (Sun, 2010b; Valvi &

Fragkos, 2012). Consequently, there is a knowledge gap in which further research in the

context of TRA has been recommended to improve the understanding of predicting

consumer intention, which is a determinant of online behavior to accept, use, or adopt

electronic commerce technology (Al-Majali, 2011; Heikkinen & Livarinen, 2011; Peslak

et al., 2010). It has been posited that consumer behavior can be predicted by one’s

intention and the influence of subjective norms to engage or not engage in an activity

(Bleakley & Hennessy, 2012; Cha, 2011; Moshref Javadi et al., 2012). By addressing the

problem, a better understanding of the relationship and predictive strength of five

predictor variables towards consumer intention to adopt an electronic payment system

may have important implications for consumers and online merchants participating in

business-to-consumer transactions in the United States. A better understanding of

consumer concerns could help online vendors improve their online payment systems,

increase consumer adoption and sales, and predict online purchase intentions (Cheney et

al., 2012; Gao & Wu, 2010; Hoehle, et al., 2012; Vanetti, 2010).

Purpose of the Study

The purpose of this quantitative ex post facto study was to examine the

relationship and test the predictive strength between each of the five predictor variables

and one criterion variable. Consumers’ propensity to trust, perceived privacy, perceived

security, subjective norms, and recognition of third party existence were the predictor

variables, while consumer intention to adopt an electronic payment system was the

criterion variable. Two previously published survey instruments were chosen for this

study because the context was specific to the constructs under investigation (Cheung &

Lee, 2001; Crespo & Rodriguez, 2008). The CTIS survey (Cheung & Lee, 2001) was

used to measure consumers’ propensity to trust, perceived privacy, and perceived

security. Subjective norms (i.e., influence of family, friends, and media) and consumer

intention to adopt an electronic payment system was measured by the IPI survey (Crespo

& Rodriguez, 2008). The number of participants was determined by conducting a power

analysis (Faul et al., 2009). A minimum of 92 consumers, 18 years of age and older, who

conduct online business-to-consumer transactions in the United States, were needed to

participate. The survey host managed the solicitation of participants through its

SurveyMonkey Audience database. TRA theorists purport that consumer behavior is

governed by the intention to perform a specific behavior based on one’s attitude and

subjective norms (Awa et al., 2010; Cha, 2011). Consumer behavior may be predicted by

one’s intention and the influence from friends, family, or media to engage in an activity

(Bleakley & Hennessy, 2012; Li & Karahanna, 2012; Moshref Javadi et al., 2012;

Vachon, 2011). TRA was used as the framework to examine the relationship and test the

predictive strength between each of the five predictor variables and one criterion variable.

The results of this study may provide online merchants information to improve their

electronic payment systems, increase consumer acceptance of this technology, and

develop methods to sustain long-term profitability (Cheney et al., 2012; Ozkan et al.,

2010; Valvi & Fragkos, 2012).