Comparison of Consumers’ behavioral intention...

Dr Ludwig Chang, FDS

Comparison of Consumers’ behavioral intention towards

Credit Card Mobile Payment and Octopus Mobile Payment

in Hong Kong

BY

Lo Ka Foon

12001082

Information Systems and e-Business Management Concentration

An Honours Degree Project Submitted to the

School of Business in Partial Fulfillment

of the Graduation Requirement for the Degree of

Bachelor of Business Administration (Honours)

Hong Kong Baptist University

Hong Kong

April 2014

Acknowledgement

I would like to take this opportunity to express my sincerely thankfulness and

indebtedness to my supervisor, Dr. Ludwig Chang M. K., for his supervision and

guidance. Dr. Chang is always willing to share his precious opinions and insights to

me during meetings. His profession in conducting research and valuable advises have

provided me with a clear direction of how to perform a research and broadened my

horizons. It would be impossible for me to finish this meaningful research without the

supports from Dr. Chang.

Nevertheless, I would like to thank all of the people who have helped me to finish

this research, especially the Professors in HKBU. The knowledge they shared with me

has equipped me with the ability in performing the study. Without the participation of

the respondents, I cannot gain data to investigate the hypotheses too.

Abstract

With recent growth of technology, mobile payment has become increasingly

popular in our daily lives. Users can pay for goods and services with their mobile

devices. In Hong Kong, the most recent mobile payment methods via Near Field

Communication (NFC) technology are Credit Card Mobile Payment provided by

banks and Octopus Mobile Payment provided by Octopus Card Limited (OCL).

Previous researches have investigated the relationships between different variables

and Behavioral Intention with Technology Acceptance Model (TAM) in different

countries. This study tests a model of acceptance of mobile payment based on Unified

Technology Acceptance and Use of Technology (UTAUT) model in Hong Kong. The

research model contains 8 factors that may affect consumers’ behavioral intention,

including Performance Expectancy, Effort Expectancy, Social Influence, Facilitating

Conditions, Trialability, Communicability, Perceived Risk and Personal

Innovativeness in the Domain of Information Technology. Behavioral Intentions of

Credit Card mobile payment and Octopus mobile payment will be analyzed separately.

Comparison was made based on the factors influencing these two different mobile

payment methods.

The results indicated that different factors have different effect on different mobile

payment methods. The Behavioral Intention of each mobile payment methods is

affected by four variables. This report will discuss and attempt to explain why some

factors are significant or insignificant for each mobile payment methods. Some

implications will be provided for banks and Octopus Card Limited too.

Table of Contents

1 Introduction ..................................................................................................................... 1

1.1 Background .................................................................................................... 1

1.2 Research Objective ........................................................................................ 2

2 Literature review ............................................................................................................. 5

2.1 Technology acceptance theories ................................................................... 5

2.2 The Buyer Decision Process for new Product ............................................. 6

2.2.1 Individual Differences in Innovativeness .................................................... 7

2.2.2 Product Characteristics ................................................................................ 8

2.2.2.1 Relative advantage ................................................................ 8

2.2.2.2 Compatibility ......................................................................... 9

2.2.2.3 Complexity ............................................................................. 9

2.2.2.4 Divisibility .............................................................................. 9

2.2.2.5 Communicability ................................................................. 10

2.3 Perceived Risk ............................................................................................. 10

2.4 Studies on Mobile Payment Adoption ....................................................... 11

3 Research model and hypotheses ................................................................................... 13

3.1 Behavioral Intention.................................................................................... 13

3.2 Trialability ................................................................................................... 14

3.3 Communicability ......................................................................................... 14

3.4 Performance Expectancy ............................................................................ 15

3.5 Effort Expectancy ........................................................................................ 15

3.6 Social Influence ............................................................................................ 16

3.7 Facilitating Conditions ................................................................................ 16

3.8 Perceived Risk ............................................................................................. 17

3.9 Personal Innovativeness in the Domain of Information Technology ...... 18

4 Research Methodology .................................................................................................. 20

4.1 Construct measurement .............................................................................. 20

4.2 Design of questionnaire ............................................................................... 20

4.3 Data collection procedure ........................................................................... 21

4.4 Survey Response .......................................................................................... 21

5 Data Analysis and Result .............................................................................................. 22

5.1 Reliability Test ............................................................................................. 22

5.2 Correlation Analysis.................................................................................... 22

5.3 Hypothesis Testing ...................................................................................... 23

5.3.1 Factors affecting Behavioral Intention (BI) .............................................. 23

5.3.1.1 Credit Card Mobile Payment ............................................ 23

5.3.1.2 Octopus Mobile Payment ................................................... 24

5.3.2 Factors affecting Performance Expectancy (PE) ...................................... 25



5.3.3 Factors affecting Effort Expectancy (EE) ................................................. 27



5.4 Comparison of Mobile Payment Methods ................................................. 28

5.5 Structural models and Summaries of the results ...................................... 28

6 Discussion and Implications ......................................................................................... 31

6.1 Facilitating Conditions and Trialability .................................................... 31

6.2 Performance Expectancy and Perceived Risk .......................................... 32

6.3 Communicability and Personal Innovativeness in the Domain of

Information Technology ................................................................................................... 34

6.4 Effort Expectancy and Social Influence .................................................... 35

6.5 Moderating effects of PIIT ......................................................................... 36

7 Limitation ....................................................................................................................... 37

8 Conclusion ...................................................................................................................... 38

9 References ...................................................................................................................... 39

Appendix 1: Survey Items..................................................................................................... 44

Appendix 2: Questionnaire (English Version) .................................................................... 47

Appendix 3: Questionnaire (Chinese Version) ................................................................... 50

Appendix 4: Demographic profile of respondents .............................................................. 52

Appendix 5: Descriptive Statistics for Factors.................................................................... 53

Appendix 6: Results of reliability test .................................................................................. 54

Appendix 7: Correlation of Constructs for Credit Card Mobile Payment ...................... 56

Appendix 8: Correlation of Constructs for Octopus Mobile Payment ............................. 56

Appendix 9: UTAUT Model ................................................................................................. 57

Appendix 10: Adopter Group............................................................................................... 57

1

1 Introduction

1.1 Background

Due to the development of Near Field Communication (NFC) which is a

contactless communication that allows users to send data over a NFC compatible

device, mobile payment can be used to perform different types of transactions.

Mobile payment is a method that consumers make use of their mobile phones to

make payments for goods or services (HSBC, n.d.). There are various types of

mobile payments, such as direct mobile billing and contactless NFC (Wikipedia,

2013). According to Amoroso and Magnier-Watanabe (2012), it is any payment

that initiates, authorizes and confirms a commercial transaction through a mobile

device. Mobile payment is defined as using a mobile phone for contactless

payment in some merchants through a specific device provided by Visa,

MasterCard and Octopus Card Limited (OCL) in this paper.

Banks such as The Hongkong and Shanghai Banking Corporation (HSBC)

started to provide mobile payment (NearFieldCommunication.org, n.d., HSBC,

n.d.; Hang Seng Bank, n.d.). Users can make credit card payment via their mobile

phones with NFC. Banks make use of NFC to provide mobile payment by using

Visa payWave and MasterCard PayPass (Visa, n.d.; Master Card, n.d.). Customers

can use them to make payment through tapping mobile phone on a reader after

installing a specific application on the mobile phone.

Similar application can be found in Mobile Suica which allows i-mode phones

to be used as normal train tickets (Amoroso et al, 2012). Suica card is a

contactless smart card for fares payment on JR East railway network, similar to

the usage and function of Octopus card in Hong Kong. In 2006, East Japan

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Railway Company (JR East) issued mobile Suica and it got 1.5 million subscribers

after 3 years. Users just need to hold the mobile phone near the sensor to pay for

any transaction. (Wireless Watch Japan, 2005; East Japan Railway Company,

2005).

In Hong Kong, Octopus Card Limited (OCL) also spotted the opportunity in

mobile payment. Recently, it introduced a new service – Octopus Mobile Payment

Service to compete with the mobile payment provided by banks which use NFC to

transmit payment information (Yahoo!, 2013; Octopus, 2013). Once consumers

adopted Octopus Mobile SIM (OMS), they can make payment by just tapping

their smartphone, similar to the use of octopus.

1.2 Research Objective

As increasing number of merchants start accepting mobile payment via credit

cards or octopus cards and more devices can support mobile payment, Hong Kong

market seems to be ready to adopt NFC mobile payment services and there is no

doubt that mobile commerce will become the future trend (Ho, 2012). However, a

report from MasterCard (2012) pointed out that although it is easy for companies

in Hong Kong to adapt new technologies and consumers are experienced at using

contactless payments because of Octopus card, consumers in Hong Kong are less

familiar and less willing to use Mobile Payment. For Hong Kong, it is just in an

initial stage of mobile payment adoption (Au et al., 2008). Although banks have

introduced mobile payment for more than one year, not many people are using or

willing to use this technology. There can be a number of reasons influencing their

intention.

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One of the reasons that deter Hong Kong citizens from adopting mobile

payment may be the restrictions. Although mobile payment provides a lot of

convenience for consumers, many restrictions and processes need to be taken

before they can really use the mobile payment. For example, Hang Seng Mobile

Payment (Application from Hang Seng Bank to provide mobile payment) only

allows Hang Seng MasterCard Credit Card Principal Card holders and

PCCW-HKT mobile network customers to use this service (Hang Seng Bank, n.d.).

Even for existing PCCW-HKT customers, they have to exchange for the NFC

SIM Card and it is not applicable for prepaid card, 2G mobile plan or designated

mobile plan. It may decrease their intention to use this service as they expect they

have to put much effort to adopt the new technology.

People may also believe that it is risky to use mobile payment. In 2010, OCL

had been discovered that it had sold Octopus card holders’ personal information to

third parties since 2006 (Lui & Mao, 2010). After this scandal, card holders may

be more concerned about the privacy and security problems of Octopus. People

may become less willing to use mobile payment because they are afraid that more

personal data will be discovered and misused by the OCL as well as banks.

Moreover, since Octopus Card service is well developed, customers may find

that the functions of credit card mobile payment and octopus mobile payment are

very similar with the traditional octopus. Therefore, they may take the view that it

is not necessary for them to use this service. They can perform transaction easily

using octopus card without adopting mobile payment.

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Many factors can influence customers’ intention to use mobile payment, for

example, availability, reliability and acceptance (Amoroso et al., 2012). This paper

has two main purposes. First, this paper aims at identifying the variables that

would influence consumers’ intention to adopt mobile payment. Therefore, we can

understand that how mobile payment launchers can improve the service to

enhance its usage. Second, factors affecting customers’ intention will be compared

between credit card mobile payment and Octopus mobile payment. When

customers choose to use mobile payment, the factors affecting consumers’

intention to adopt credit card mobile payment may be different from the factors

influencing consumers’ intention to adopt Octopus mobile payment. This paper

will also study the differences.

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2 Literature review

In this section, literature review and theoretical background about technology

acceptance theories, buyer decision processes for a new product, perceived risk and

mobile payment will be discussed.

2.1 Technology acceptance theories

Several models have been built to elaborate the intention or acceptance for a

user to adopt a new technology. Technology acceptance model (TAM) is one of

the most popular models among technology acceptance theories. Technology

acceptance model is a research model that commonly used in explaining the IT

adoption behavior of users. TAM has two belief variables which are perceived

usefulness and perceived ease of use (Kim et al., 2009). They will directly and

indirectly influence an individual’s intention to adopt the technology. Perceived

usefulness is the extent that an individual takes the view that using that technology

will improve the performance of his or her job while perceived ease of use is the

extent that an individual takes the view that he or she will find no difficulty in

using that technology. TAM2 has extended the origianl TAM, which included

more variables (Venkatesh et al., 2000). The antecedents of perceived usefulness

include subjective norm, image, job relevance and output quality while

voluntariness and experience are the new moderators. TAM3 is the latest TAM

which added two main aspects: four anchors which included computer

self-efficacy, perceptions of external control, computer anxiety and computer

playfulness as well as two adjustments which are objective usability and perceived

enjoyment (Venkatesh et al., 2008).

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Since TAM mainly emphasizes on perceived usefulness and perceived ease of

use, and includes too many constructs that may not affect consumers’ intention to

adopt mobile payment directly or indirectly, in this paper, a model combined

different models, unified theory of acceptance and use of technology (UTAUT),

will be used as UTAUT provides the manner in a more complete and realistic than

TAM (Rosen, 2005).

In UTAUT model which has been shown in Appendix 9, there are four direct

determinants and key moderators (Venkatesh et al., 2003). The four constructs

affecting consumers’ acceptance and usage behavior are performance expectancy,

effort expectancy, social influence and facilitating conditions. The moderators in

UTAUT are gender, age, voluntariness and experience. Performance expectancy is

an extent that an individual takes the view that he or she can gain in job

performance by using the new system. Effort expectancy is an extent that a person

takes the view that the new system is not difficult for him or her to use. For social

influence, it is an extent that a person takes the view that others think the new

system should be used. Facilitating conditions represent an extent that a person

takes the view that there is enough support to use the new system. When

comparing to TAM, some constructs are considered as the same as the constructs

in UTAUT, Perceived usefulness and perceived ease of use in TAM are the same

as performance expectancy and effort expectancy respectively (Kim et al., 2010).

2.2 The Buyer Decision Process for new Product

According to Kotler and Armstrong (2010), adoption process is the process a

person goes through the first learning about a new product to final adoption

where this product can be a good, service, or idea that is new from the

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perspective of the potential customers. There are five stages in the adoption

process including awareness, interest, evaluation, trial and adoption. Some

consumers go through these stages quickly while others go through slowly. It

depends on individual differences in innovativeness and product characteristics.

2.2.1 Individual Differences in Innovativeness

Depending on the readiness for consumers to try the new product, there

are five adopter groups including innovators, early adopters, early majority,

late majority and laggards as shown in the Appendix 10. It depends on the

time between an innovation is introduced and a customer use it.

Since credit card mobile payment and Octopus mobile payment are new

technology in Hong Kong and only small numbers of people adopt this

technology according to MasterCard’s report (MasterCard, 2012), they

cannot be separated from the above adopter groups. In this paper, analysis

will be done based on participants’ characteristics in innovativeness instead

of separating them into several adopter groups.

To evaluate the level of consumers’ willingness to attempt new

information technology, PIIT, which refers to Personal Innovativeness in the

Domain of Information Technology, has been introduced in 1998 (Agarwal et

al., 1998). As PIIT is considered to be the willingness of a consumer to

experiment innovation, marketers can target on the consumers with higher

innovativeness first to gain early sales and word of mouth (Rosen, 2005).

In a recent study, innovative consumers have four main characteristics,

8

including a consumer’s willingness to make changes in things and concepts,

a consumer’s capability to affect other people to adopt innovative things and

concepts, a consumer is supportive in tackling problems and deciding

decisions in a social system or an organization, and the rate and time of

adoption of the aforementioned changes in a functional relationship (Ho et al.,

2011).

Therefore, we can expect that consumers with higher PIIT will have higher

intention to adopt mobile payment and may be affected differently by the

antecedents.

2.2.2 Product Characteristics

Rogers (2010) introduced five product characteristics that can affect the

adoption’s rate of a new product, including relative advantage, compatibility,

complexity, divisibility and communicability. Since some of them are nearly

the same as the constructs in UTAUT model, the characteristics and

constructs in UTAUT with similar meaning will be represented by constructs

in UTAUT.

2.2.2.1 Relative advantage

In Innovation Diffusion Theory (IDT), relative advantage is the extent

that a consumer perceives that an innovation is better than its precursor

(Moore et al., 1991). It emphasizes on the perception of consumers

rather than the objective advantage of the innovation. It can be evaluated

by several factors, such as convenience and economic gains (Ho et al.,

2011). It refers to consumers’ perception rather than objective advantage

9

of the innovation. Since it is a construct in IDT and it was combined

with other constructs to become a root construct of “Performance

Expectancy” in UTAUT, we will treat relative advantage as performance

expectancy in this paper (Venkatesh et al., 2003).

2.2.2.2 Compatibility

Since compatibility, which represents the extent that an individual

perceives that an innovation is constant with their existing values,

experiences of potential and desires in IDT, is also combined with

constructs from different theories or models to be the root construct of

“Facilitating Conditions”, it will be preserved as facilitating conditions

in this paper (Moore et al., 1991; Venkatesh et al., 2003).

2.2.2.3 Complexity

Complexity is the extent that a consumer perceives that a new

technology is relatively difficult for them to understand and use

(Thompson et al., 1991). According to Venkatesh et al. (2003),

complexity is one of the root constructs for “Effort Expectancy” in

UTAUT. Therefore, complexity will be treated as effort expectancy in

this paper.

2.2.2.4 Divisibility

Divisibility is the extent that an innovation may be experimented on a

limited basis (Kotler et al., 2010). A new product that can be tried will

increase the adoption because the uncertainty will be decreased during

experiment.

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2.2.2.5 Communicability

Communicability is the extent that extent when consumers use the

new product, the result can be observed or described to others (Kotler et

al., 2010). If a consumer perceives that the results of using a new

product are easy to observe, he or she will become more likely to use it.

2.3 Perceived Risk

The level of perceived risk will affect the decision of customers (Lu et al.,

2005). According to Swilley (2010), perceived risk in mobile payment is the loss

of data because of credit card fraud. It can also be considered as the expectations

of negative consequences or attitudes toward providing information to the seller

via mobile device (Amoroso et al., 2012). Risk can be divided into several

categories, including physical risk (PHR), functional risk (FUR), social risk

(SOR), time-loss risk (TLR), Financial risk (FIR), Opportunity cost risk (OCR)

and information risk (INR) (Lu et al., 2005). Each of them refers to different

uncertainties that consumers may face. Since not all risks are appropriate or

relevant to mobile payment, perceived risk in this paper will be defined as the

loss of data and misuse of data. Potential credit card and octopus mobile payment

users may be afraid of the possibilities that their information will be stolen or

misused for transactions or other purpose without their permission after they

installed or used credit card mobile payment.

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2.4 Studies on Mobile Payment Adoption

Mobile payment is a specific form of electronic manner to handle payment

(Schierz et al, 2010). In the past few decades, a number of studies about mobile

payment have been done to discuss the factors that are significant in affecting

consumers’ perspectives towards mobile payment.

In a multi-country study which applied Actor Network Theory (ANT) for

identifying factors that affect mobile payment adoption, it has found that the

constructs that can influence mobile payment adoption are the degree of synergy

between the macro-actors in that country, how consumers associate values to it,

the relationship between the primary point of contact and the consumers, how

correctly the micro-environment of a micro-actor is identified, market conditions,

and the presence of catalysts (Warren et al, 2008). It has also pointed out that

different patterns of mobile payment adoption will be different for different

countries.

For customer loyalty in mobile payment, a study in Iran has found that security,

customer satisfaction, perceived risk, perceived usefulness, perceived ease of use,

customization and responsiveness are the most essential factors (Sanayei et al,

2011).

A study about mobile suica which is similar to mobile Octopus has proposed a

comprehensive model to illuminates variables that would affect mobile payment

adoption, including perceived usefulness, attitude, facilitating conditions,

perceived value, perceived security and privacy, social influence, trust, perceived

risk, and attractiveness (Amoroso et al, 2012).

12

Studies have also proved that perceived cost, perceived risk, trust, relative

advantage, image, perceived compatibility, perceived security, perceived

usefulness, perceived ease of use, individual mobility, subjective norm,

availability, confidentiality, privacy, processing integrity will also have positive

or negative impacts on consumers’ adoption or behavioral intention or attitude of

mobile payment (Lu et al, 2011; Schierz et al, 2010; Kim et al, 2010; Meharia,

2012).

Various perspectives or theories can be used to explain behavioral intention

and adoption of mobile payment and all of them have developed a better

understanding of mobile payment for us. Most of the studies focused on the effect

of and what would affect perceived usefulness and perceived ease of use on

mobile payment adoption from TAM. Since limited studies are established based

on UTAUT model or discussed the situation in Hong Kong, this paper will further

studies mobile payment in Hong Kong using UTAUT model.

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3 Research model and hypotheses

In order to identify the factors that will affect consumers’ behavioral intention to

use mobile payment, the following hypotheses have been developed and the

comparison of credit card mobile payment and octopus mobile payment will be

discussed in the later parts. The research model to be studied is shown in Figure 1,

which is mainly developed based on UTAUT model with some additional factors. The

research model includes four constructs from UTAUT, two constructs from IDT,

perceived risk, and individual differences in innovativeness as moderators.

Figure 1 – Research Model

3.1 Behavioral Intention

Behavioral Intention is the likelihood that consumer will use an innovation

(Venkatesh et al., 2003). With higher behavioral intention, a consumer will

14

become more likely to use a new technology. There are several antecedents that

may affect an individual’s behavioral intention.

3.2 Trialability

Trialability has the same meaning as divisibility which is the extent that a

person can experiment and try the innovation (Roger, 2010). Roger (2010) stated

that trialability will affect consumers’ intention to use goods. Also, a positive

relationship between trialability and adoption intention has been found. It can

strengthen the intention to adopt a new product and eliminate the insecurity about

an innovation (Ho et al., 2011). Since consumers have to register or fulfill

particular requirements before they use credit card mobile payment or octopus

mobile payment, it will be difficult to let consumers try before they adopt and

they will, therefore, have lesser intention to adopt mobile payment.

H1. Triablability will have a positive effect on Behavioral intention.

3.3 Communicability

Communicability is one of the antecedents of result demonstrability. It was

found to have a positive relationship to behavioral intention (Wahid, 2010). In

TAM2 model and TAM3 model, result demonstrability shows a positive effect on

perceived usefulness which is defined as performance expectancy in this study

(Venkatesh et al., 2008). Result demonstrability is the degree that an individual

takes the view that the outcomes are tangible, observable and communicable for

using the system (Moore et al., 1991). It was also found to have a positive

relationship with perceived ease of use which is same as effort expectancy

(Kacmar et al., 2009). Since communicability is the antecedent of result

15

demonstrability, communicability will, therefore, be treated to have the same

effect as result demonstrability in this paper. If customers believe that mobile

payment is communicable, their performance expectancy, effort expectancy and

behavioral intention will increase.

H2a. Communicability will have a positive effect on performance expectancy.

H2b. Communicability will have a positive effect on effort expectancy.

H2c. Communicability will have a positive effect on behavioral intention.

3.4 Performance Expectancy

Performance Expectancy and perceived usefulness in UTAUT and TAM are

the most powerful methods to illuminate behavioral intention to adopt a new

system (Park et al., 2007). Perceived usefulness was found to have strong effect

on consumers’ attitude to use mobile payment system (Meharia, 2012). If

consumers find that adopting credit card or octopus mobile payment can help

them to perform their jobs better, their intentions to use these kinds of mobile

payment methods will increase.

H3. Performance Expectancy will have a positive effect on behavioral intention.

3.5 Effort Expectancy

Effort expectancy has been found to affect behavioral intention positively in

several studies (Park et al., 2007; Im et al., 2011). Meharia (2012) has also found

that perceived ease of use will affect consumers’ attitude towards adopting mobile

16

payment. The willingness of consumers to use credit card or octopus mobile

payment will increase if they think that the system is easy for them to use.

H4. Effort expectancy will have a positive effect on behavioral intention.

3.6 Social Influence

Social influence has a direct effect on behavioral intention under mandatory

settings and inexperienced system environments even consumers do not have the

intention to perform a behavior originally if they think others consider that they

have to use it or being motivated by some referents (Venkatesh et al., 2000; Park

et al., 2007). It was found that attitude on using mobile technologies was affected

by social influence (Park et al., 2007). However, mobile payment methods are

newly offered in Hong Kong. There may be little referents since not many people

are familiar with the technology. Moreover, consumers can choose to use these

methods or not. Thus, social influence may not have great impact in behavioral

intention yet.

H5. Social Influence will have a positive effect on behavioral intention.

3.7 Facilitating Conditions

In UTAUT, facilitating conditions are the antecedents of Use Behavior instead

of affecting behavioral intention directly (Venkatesh et al., 2003). Although

facilitating conditions were not significant in elaborating behavioral intention in

UTAUT, it was found to slightly affect the behavioral intention of mobile

technologies in early adoption stage (Park et al., 2007). As Hong Kong does not

have many merchants to support the use of credit card mobile payment yet while

17

octopus mobile payment can be used widely once you adopted it because of the

existing octopus system, the relationship between facilitating conditions and

behavioral intention may be stronger for credit card mobile payment.

H6. Facilitating Conditions will have a positive effect on behavioral intention.

3.8 Perceived Risk

Cunningham (1967) defined risk as the uncertainty and adverse consequences a

consumer feels or perceives when he or she is buying a product. Perceived risk

will play a significant role in affecting the perceived usefulness and perceived

ease of use but it will not affect the behavioral intention directly (Lu et al., 2005).

The lower the level of perceived risk is, the higher the adoption behavior,

perceived ease of use and perceived usefulness will be (Wafa, 2009). Consumers’

perception of risk using mobile payment systems will diminish their intention to

adopt these systems (Lu et al., 2011). As the risk is very high if users lose their

phones or their information is stolen by others, the perceived risk for customers

may have a great impact on the behavioral intention.

H7a. Perceived risk will have a negative effect on performance expectancy.

H7b. Perceived risk will have a negative effect on effort expectancy.

H7c. Perceived risk will have a negative effect on behavioral intention.

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3.9 Personal Innovativeness in the Domain of Information Technology

Based on different consumers’ innovativeness, they may have different reaction

towards a new technology. The higher level of Personal Innovativeness in the

Domain of Information Technology (PIIT) is, the higher opportunity that a person

will have a behavioral intention and adopt the new technology earlier than others

with lower level of PIIT since they are more willing to try new things (Agarwal et

al., 1998). Innovativeness of consumer affects behavioral intention significantly

and positively (Ho et al., 2011). Innovativeness has been found to affect the use

of m-services positively and innovative consumers will be more willing to try

new m-services (Mort et al., 2007). Apart from being an essential predictor of

behavioral intentions, Rosen (2005) also found that PIIT can moderate the effect

of usefulness and ease of use on intention. PIIT can work as the moderators

among three UTAUT constructs and behavioral intention, including relative

advantage, ease of use and compatibility (Agarwal et al., 1998). As innovative

individuals appear to be more curious and active in seeking information, they will

find more information about a new technology to understand it before they adopt

and think the innovation is more useful and easier to use than less innovative

consumers (Kim et al., 2012). This contributes to a moderating effect between

performance expectancy and effort expectancy with behavioral intention.

Moreover, the relationship will be moderated because innovative consumers have

higher willingness to make changes and they are more capable to deal with

uncertainty (Ho et al., 2011; Agarwal et al., 1998). Their abilities to cope with

difficulties make them believe that they do not need to pay much effort to adopt

mobile payment. Thus, more innovativeness people will be affected less by the

complexity of the new technology.

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H8a. PIIT will have a positive effect on behavioral intention.

H8b. PIIT will moderate the relationship between performance expectancy and

behavioral intention.

H8c. PIIT will moderate the relationship between effort expectancy and

behavioral intention.

According to past researches, fourteen hypotheses have been developed to show the

proposed relationships between different variables. To examine whether the

hypotheses are tenable, data will be needed to prove that the relationships exist. A

research has been established to collect the data.

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4 Research Methodology

4.1 Construct measurement

To investigate consumer’s intention to use mobile payment and compare the

difference between credit card and octopus mobile payment, data collection had

be done based on a structured questionnaire with multi-item measures to ensure

the validity of each instrument. The research model in this study includes 9

constructs; their measurements were adapted from previous studies and revised to

fit the study. A seven-point Likert scale from strongly disagree to strongly agree

was used in the questionnaire to measure the constructs. In order to ensure all

participants know what mobile payment is, participants were given the definition

of mobile payment at the beginning of the questionnaire. The survey items can be

found in the Appendix 1.

4.2 Design of questionnaire

The questionnaire contains four main parts. The first part is a screening

question to make sure that participants are smartphone users. Non smartphone

users were not required to continue the questionnaire. The second part which

measures participants’ perception of mobile payment includes eight constructs –

divisibility, communicability, performance expectancy, effort expectancy, social

influence, facilitating conditions, perceived risk and behavioral intention. This

part has been divided into two sections, credit card mobile payment and octopus

mobile payment, to evaluate participates’ perception of two different mobile

payment methods. The third part evaluates participants’ personal innovativeness

in the domain of information technology. The forth part emphasizes on the

demographic data of participants, such as age and gender. The design of the

questionnaire can be found in Appendix 2 and Appendix 3.

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4.3 Data collection procedure

In this study, data was collected via two methods – online and offline.

Qualtrics.com was used for developing the survey online and online survey was

conducted online via social networking – Facebook, where participates were

invited to conduct the web-based research. Questionnaires were also distributed

in hardcopy. Both English and Chinese version questionnaires have been

developed to ensure participants can fully understand all questions. English

version questionnaires were distributed online using Qualtrics.com while Chinese

version questionnaires were distributed offline. The target of this study focused

on smartphone users who aged between 18 – 65 years old.

4.4 Survey Response

During 3rd

of March to 23rd

of March, 218 questionnaires have been distributed

online and offline. 205 valid responses out of 218 questionnaires were collected

and utilized for data analysis. The remaining questionnaires have been considered

to be invalid because of incompleteness of the questionnaires or invalid responses.

Among these 205 usable questionnaires, there are 97 male respondents and 108

female respondents. The main age group of the respondents is 18 – 25 years old

and around half of the respondents are student. Demographic profile of the

respondents is shown in the Appendix 4.

22

5 Data Analysis and Result

To understand which variables will affect the behavioral intention of adopting

mobile payment and the difference between credit card mobile payment and octopus

mobile payment, statistical analysis will be examined in the following part. Since the

study contains two different mobile payment methods, two different analyses will be

executed separately with the aid of Statistical Package for the Social Sciences (SPSS)

to test the model.

5.1 Reliability Test

In order to test the reliability of the data, reliability analysis was performed by

using Cronbach’s Alpha. The scale will be considered to be consistent if

Cronbach’s Alpha is larger than 0.7. The results of reliability test can be found in

Appendix 6. Most of the scales are reliable and consistent with the Cronbach’s

Alpha larger than 0.7. FC3 and PIIT3 for both payment methods were deleted as

the Cronbach’s Alpha of their corresponding construct is lower than 0.7 and

deleting them brought the values higher than 0.7. CO4 for both payment methods

will also be deleted as the reliability for Communicability will be significantly

increased if it is deleted. The adjusted results of reliability test and the descriptive

statistics of factors are shown in the Appendix 5.

5.2 Correlation Analysis

Pearson’s correlation analysis has been performed to investigate the

relationships between two variables with a range from 1 (positive relationship) to

-1 (negative relationship). If the value is closer to 1 or -1, it indicates a stronger

relationship between two variables. The results have been shown in the Appendix

7 and Appendix 8.

23

5.3 Hypothesis Testing

Linear regression was performed to find the relationships between independent

variables and dependent variables. R Square represents the strength of association

between dependent variables and independent variables. Since the hypotheses are

one-tailed test, the relationships will be considered as significant if the p-value

(sig.) printout from SPSS is lower than 0.1, which is equivalent to set the Alpha

value to 0.05. Hence, the below p-value will be divided by 2.

5.3.1 Factors affecting Behavioral Intention (BI)

Behavioral Intention is a dependent variable that is affected by both

independent variables and moderators. The independent variables affecting

Behavioral Intention is Performance Expectancy, Effort Expectancy, Social

Influence, Facilitating Conditions, Trialability, Communicability, Perceived

Risk and Personal Innovativeness in the Domain of Information Technology

(PIIT). PIIT also acts as a moderator of Performance Expectancy and Effort

Expectancy between Behavioral Intention.

5.3.1.1 Credit Card Mobile Payment

The hypothesis testing result of Behavioral Intention is shown in table

7. The R Square is 0.583, which means 58.3% of variance in Behavioral

Intention can be explained by its independent variables. As the p-value

for Performance Expectancy, Facilitating Conditions, Trialability and

Perceived Risk are smaller than 0.05, these independent variables are

positively related to Behavioral Intention. However, no significant effect

can be found between Behavioral and other independent variables.

Moderators do not have significant effect too. Therefore, only H1, H3,

24

H6 and H7c are supported and H2b, H4, H5, H8a, H8b and H8c are not

supported by the findings for Credit Card Mobile Payment.

Table 1 – Hypothesis Testing of BI (Credit Card Mobile Payment)

Behavioral Intention (R Square: 0.583)

Model B Std. Error Beta T Sig.

(p-value)

(Constant) -.158 .350 -.450 .327

PE .193 .078 .187 2.487 .007*

EE .126 .096 .119 1.323 .094

SI -.008 .064 -.007 -.126 .450

FC .215 .082 .221 2.634 .005*

TR .219 .077 .222 2.844 .003*

CO .113 .069 .110 1.624 .053

PR .103 .045 .118 2.304 .011*

PIIT .023 .069 .020 .327 .372

Interaction of

Performance

Expectancy and

PIIT

-.023 .059 -.032 -.394 .347

Interaction of

Effort Expectancy

and PIIT

.012 .059 .017 .201 .421

5.3.1.2 Octopus Mobile Payment

The hypothesis testing result of Behavioral Intention is shown in table

8. The R Square is 0.660, which means 66.0% of variance in Behavioral

Intention can be explained by its independent variables. As the p-value

for Facilitating Conditions, Trialability, Communicability and PIIT are

smaller than 0.05, these independent variables are positively related to

Behavioral Intention. However, no significant effect can be found

between Behavioral and other independent variables. Moderators do not

25

have significant effect too. Therefore, only H1, H2c, H6 and H8a are

supported and H3, H4, H5, H7c, H8b and H8c are not supported by the

findings for Octopus Mobile Payment.

Table 2 – Hypothesis Testing of BI (Octopus Mobile Payment)

Behavioral Intention (R Square: 0.660)


(p-value)

(Constant) -.130 .344 -.377 .354

PE .070 .057 .076 1.242 .108

EE .094 .087 .088 1.087 .139

SI -.015 .064 -.013 -.226 .411

FC .252 .082 .239 3.093 .001*

TR .109 .065 .113 1.673 .048*

CO .324 .083 .312 3.918 .000*

PR .045 .045 .043 .997 .160

PIIT .142 .061 .121 2.315 .011*

Interaction of

Performance

Expectancy and

PIIT

-.022 .053 -.031 -.411 .341

Interaction of

Effort Expectancy

and PIIT

.013 .053 .019 .253 .401

5.3.2 Factors affecting Performance Expectancy (PE)

Performance Expectancy is a dependent variable that is determined by

Communicability and Perceived Risk.


The hypothesis testing result of Performance Expectancy is shown in

table 3. The R Square is 0.307, which means 30.7% of variance in

26

Performance Expectancy can be explained by Communicability and

Perceived Risk. Both Communicability and Perceived Risk are

positively related to Performance Expectancy as their p-values are 0.000.

Therefore, H2a and H7a for Credit Card Mobile Payment are supported.

Table 3 – Hypothesis Testing of PE (Credit Card Mobile Payment)

Performance Expectancy (R Square: 0.307)


(p-value)

(Constant) 1.614 .358 4.508 .000

CO .434 .058 .439 7.487 .000*

PR .263 .050 .309 5.273 .000*


The hypothesis testing result of Performance Expectancy is shown in

table 4. The R Square is 0.373, which means 37.3% of variance in

Performance Expectancy can be explained by Communicability and

Perceived Risk. With p-value = 0.000, Communicability is positively

related to Performance Expectancy. However, no significant effect can

be found between Perceived Risk and Performance Expectancy.

Therefore, H2a is supported and H7a is not supported by the findings.

Table 4 – Hypothesis Testing of PE (Octopus Mobile Payment)

Performance Expectancy (R Square: 0.373)


(p-value)

(Constant) 1.517 .405 3.743 .000

CO .671 .063 .599 10.712 .000*

PR .094 .062 .084 1.504 .067

27

5.3.3 Factors affecting Effort Expectancy (EE)

Effort Expectancy is a dependent variable that is determined by

Communicability and Perceived Risk.


The hypothesis testing result of Effort Expectancy is shown in table 5.

The R Square is 0.426, which means 42.6% of variance in Effort

Expectancy can be explained by Communicability and Perceived Risk.

As the p-value for Communicability and Perceived Risk are 0.000 and

0.003 respectively, both of them are positively related to Effort

Expectancy. Therefore, H2b and H7b for Credit Card Mobile Payment

are supported by the findings.

Table 5 – Hypothesis Testing of EE (Credit Card Mobile Payment)

Effort Expectancy (R Square: 0.426)


(p-value)

(Constant) 1.532 .317 4.836 .000

CO .601 .051 .625 11.710 .000*

PR .124 .044 .150 2.803 .003*


The hypothesis testing result of Effort Expectancy is shown in table 6.

The R Square is 0.619, which means 61.9% of variance in Effort

Expectancy can be explained by Communicability and Perceived Risk.

As the p-value for Communicability and Perceived Risk are 0.000 and

0.038 respectively, both of them are positively related to Effort

28

Expectancy. Therefore, H2b and H7b are supported by the findings for

Octopus Card Mobile Payment.

Table 6 – Hypothesis Testing of EE (Octopus Mobile Payment)

Effort Expectancy (R Square: 0.619)


(p-value)

(Constant) 1.035 .274 3.776 .000

CO .755 .042 .777 17.842 .000*

PR .075 .042 .078 1.790 .038*

5.4 Comparison of Mobile Payment Methods

The last thing to be performed is the comparison of the two different mobile

payment methods. Unstandardized Coefficients (B) was compared to see which

variables have a greater impact for credit card mobile payment and octopus

mobile payment. After compared the unstandardized coefficients (B) in table 1

and table 2 in section 5.3.1, Performance Expectancy, Effort Expectancy,

Trialability and Perceived Risk have been found to have a great influence for

credit card mobile payment. Facilitating conditions, Communicability and

Personal Innovativeness in the Domain of Information Technology have a higher

impact on Octopus mobile payment. Social Influence has the same level of

impact for both mobile payment methods as their unstandardized coefficients (B)

are the same.

5.5 Structural models and Summaries of the results

The structural models and summaries of the hypotheses testing results are

shown in the below tables and graphs. Summary of hypotheses testing results can

be found in table 7. It has shown that 8 out of 14 hypotheses can be supported for

29

Credit Card Mobile Payment and 7 out of 14 hypotheses can be supported for

Octopus Mobile Payment. No moderating effect can be found by the results.

Figure 2 has shown the structural models of Credit Card Mobile Payment and

Octopus Mobile Payment.

Table 7 – Summary of Hypotheses testing results

Hypotheses testing results

Path Credit Card Octopus Card

H1 Triablability Behavioral Intention

H2a Communicability Performance

Expectancy

H2b Communicability Effort Expectancy

H2c Communicability Behavioral Intention

H3 Performance Expectancy Behavioral

Intention

H4 Effort expectancy Behavioral Intention

H5 Social Influence Behavioral Intention

H6 Facilitating Conditions Behavioral

Intention

H7a Perceived risk Performance

Expectancy

H7b Perceived risk Effort expectancy

H7b Perceived risk Behavioral Intention

H8a PIIT Behavioral Intention

H8b Moderator PIIT will affect (Performance

expectancy Behavioral Intention)

H8c Moderator PIIT will affect (Effort

expectancy Behavioral Intention)

30

Figure 2 – Structural Model

31

6 Discussion and Implications

This research aims at studying the factors that would affect consumers’ behavioral

intention in using credit card mobile payment and octopus mobile payment, and

further investigating the difference of the factors between two mobile payment

methods. Nevertheless, the results have shown that not all hypotheses are supported

by the data collected. In this part, different factors that influence behavioral intention

will be discussed and some implications for credit card mobile payment and octopus

mobile payment will be provided.

6.1 Facilitating Conditions and Trialability

For both mobile payment methods, Facilitating Conditions and Trialability are

significant predictors for Behavioral Intention. Consumers still are not aware of or

familiar with mobile payment in Hong Kong. Many of them do not even know

what mobile payment is or how mobile payment can benefit their daily lives. As

mobile payment is a new thing for consumer, it would be essential for them to

have a better understanding about how to use it and where they can try it, no

matter for which mobile payment methods. The more information they know

about mobile payment, the higher intention for them to use mobile payment. If

they do not have enough resources or knowledge to get a trial on mobile payment,

they would have no interest in using it.

Therefore, it would be important for banks and Octopus Card Limited to have

more promotion about this new technology. It would attract more people to use

mobile payment if they have a better knowledge about mobile payment and

recognize the advantages of using mobile payment. There are many prerequisites

for people to adopt mobile payment, such as the model of the mobile phone and

32

mobile service provider. It is necessary to minimize the prerequisites of adopting

mobile payment too as people who have intention to use mobile payment may be

hindered from adopting it because of the prerequisites. When people find that it is

difficult for them to try it, their intention of using mobile payment may be

decreased.

6.2 Performance Expectancy and Perceived Risk

According to the results, Performance Expectancy and Perceived Risk are

significant predictors for Behavioral Intention for credit card mobile payment, but

not for octopus mobile payment.

For Performance Expectancy, it is important for the intention of using credit

card mobile payment but not octopus mobile payment because credit card mobile

payment is quite different from traditional credit card in term of their usages and

results. Whether credit card mobile payment is useful or performs better than

tradition credit card will be important for customers in determining whether they

want to adopt it. By contrast, the usage of using octopus mobile payment is nearly

same as octopus card. People are relatively familiar with what octopus card can

perform already and expect that octopus mobile payment will bring them similar

advantages. Hence, Performance Expectancy does not affect their behavioral

intention of octopus mobile payment that much.

For Perceived Risk, the outcome is like Effort Expectancy. It is a significant

predictor of Behavioral Intention to credit card mobile payment because credit

card contains more private information about the users and the potential loss is

higher in light of the bank account information. Due to these reasons, Perceived

33

Risk would have a significant impact on whether they want to adopt credit card

mobile payment. As people may consider that the risks of using octopus mobile

payment are the same as using octopus card and there is lesser information

contained in octopus card, it does not have a significant effect on octopus card.

Perceived Risk is found to have significant relationship with Effort Expectancy

for both mobile payment methods and Performance Expectancy for credit card

mobile payment too. It has a significant relationship between Perceived Risk and

Effort Expectancy because they may think they have to spend more time on

controlling the mobile payment system if there is a higher risk. Perceived Risk is a

significant predictor of Performance Expectancy for credit card mobile payment

because the risks of adopting this mobile payment method are high. Since much

sensitive information may lose or be explored, Perceived Risk will affect whether

people think it is useful. However, for octopus mobile payment, Perceived Risk

does not have a significant relationship with Performance Expectancy because the

risks of adopting octopus mobile payment are relatively low. It does not affect

Performance Expectancy very much.

As a result, banks may need to emphasize on promoting the benefits of

adopting credit card mobile payment and improve the security. By providing more

information about credit card mobile payment, people may find it is beneficial for

them to pay through mobile phones and become more willing to adopt credit card

mobile payment. Banks can also enhance their intention of adopting mobile

payment by improving the security level of the application or promoting the

security protection of the system.

34

6.3 Communicability and Personal Innovativeness in the Domain of

Information Technology

Communicability and Personal Innovativeness in the Domain of Information

Technology are significant predictors for octopus mobile payment. However, there

is no significant effect for credit card mobile payment.

For Communicability, it is a significant predictor of Behavioral Intention of

adopting octopus card mobile payment but not credit card mobile payment. As

mentioned before, octopus mobile payment is quite similar with traditional

octopus card. Since customers would believe that the outcome of using octopus

mobile payment is similar too, they may expect the outcome of using it will be as

observable and communicable as traditional octopus card. However, not many

merchants support credit card mobile payment at this stage and people will have

fewer chances to adopt it. They may not have much expectation about the

communicability of credit card mobile payment. Therefore, whether the results are

apparent may not be that important for them and Communicability of credit card

mobile payment does not affect the Behavioral Intention a lot.

For Personal Innovativeness in the Domain of Information Technology (PIIT),

the outcome is the same with Communicability. It is a significant predictor of

Behavioral Intention to octopus card mobile payment because octopus mobile

payment is the latest mobile payment method using NFC function in Hong Kong.

Customers with higher level of PIIT who are fond of trying innovation will have

higher intention of using octopus mobile payment. Nonetheless, credit card

mobile payment has been launched for a few years and it is not that up-to-date

when compared to octopus mobile payment. It would have lesser effect on

35

Behavioral Intention.

Communicability is also a significant predictor for Performance Expectancy

and Effort Expectancy in both mobile payment methods. If people are able to

explain the results of using mobile payment, they will think mobile payment is

useful and easy to use. People tend to feel something is good when they can share

the benefits of using it with others.

It is suggested that Octopus Card Limited should show the results of adopting

octopus mobile payment and explain the difference between octopus mobile

payment and octopus card to its potential customers. People can, therefore,

understand or tell others the results as well as the difference, and become more

willing to adopt octopus mobile payment.

6.4 Effort Expectancy and Social Influence

Effort Expectancy and Social Influence have no significant relationship with

Behavioral Intention in this study. As not many people have a clear idea about

mobile payment, they may not know the effort they have to spend on using mobile

payment. Several studies also found that there is no significant relationship

between Effort Expectancy and Behavioral Intention (Akturan & Tezcan, 2012;

Lewis et al, 2003; Szajna, 1996). Effort Expectancy will have less impact on their

intention of adopting mobile payment with low level of experience of mobile

payment. Since not many people will tell other or be told to use mobile payment

under this situation and some of them may not know whether others think they

should use mobile payment, Social Influence does not have much influence on

Behavioral Intention too.

36

Since Effort Expectancy and Social Influence are not significant predictors,

banks and Octopus Card Limited should spend more resources on other factors

that have significant influence on Behavioral Intention, especially the Facilitating

Conditions and Trialability. They should provide more opportunities for customers

to try the mobile payment system and provide more supports for those who intend

to adopt mobile payment rather than just emphasizing on the easiness of using

mobile payment.

6.5 Moderating effects of PIIT

No moderating effect can be found to have significant relationship in this model.

PIIT is not an effective moderator between Performance Expectancy and

Behavioral Intention or between Effort Expectancy and Behavioral Intention.

People who are more innovative may not have much idea about mobile payment

too. Normally, they think an innovation is more useful and easier to use because

they are eager to do research and get more information about a new product. Since

the knowledge they have may be the same with less innovative people, they may

have similar perception about this technology with people who are less innovative.

Therefore, they would not find mobile payment is more useful or easier to use

than others.

37

7 Limitation

Although this study provides some useful insights about mobile payment, there

are still some limitations in this study.

The samples collected are not representative enough because of two reasons.

First, the sample size of this study is relatively small to represent the entire

population in Hong Kong. In order to gain a result that is more precise and

representative, the research base can be enlarged in future study. Second, the age

group and occupation group are too concentrated. Most of the respondents are

students and aged between 18 and 25. As it could affect the results of the study,

different groups of people should be interviewed to reflect their preferences in the

future research.

Moreover, only two mobile payment methods have been chosen to investigate

in this study. Indeed, many different mobile payment methods have been

developed to provide payment services with mobile devices. For example,

TaoBao has recently enabled users to pay for their purchases through mobile

devices with their Octopus Card. In future study, more mobile payment methods

can be considered.

38

8 Conclusion

To conclude, this study mainly emphasizes on evaluating the effect of different

factors and comparing credit card mobile payment with octopus mobile payment.

The findings of this study show that Facilitating Conditions and Trialability are

significant factors of Behavioral Intention for both mobile payment methods. For

Effort Expectancy and Perceived Risk, they are significant factors of Behavioral

Intention for credit card mobile payment only. For Communicability and Personal

Innovativeness in the Domain of Information Technology, they are significant

factors of Behavioral Intention for octopus mobile payment only. There is no

significant effect between Performance Expectancy and Behavioral Intention or

Social Influence and Behavioral Intention as well as the moderator.

With the insights provided in this study, banks and Octopus Card Limited can

increase consumers’ intention to adopt mobile payment based on the results. The

most crucial step they should take is to increase the promotion of mobile payment

and expand consumers’ understanding about mobile payment.

39

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Appendix 1: Survey Items

46

Notes: Reverse Scaled Item

47

Appendix 2: Questionnaire (English Version)

50

Appendix 3: Questionnaire (Chinese Version)

52

Appendix 4: Demographic profile of respondents

Division Frequency Percent

Gender

1. Male 97 47.3 %

2. Female 108 52.7 %

Age

1. 18 – 25 125 61 %

2. 26 – 35 23 11.2 %

3. 36 – 45 29 14.1 %

4. 46 – 55 23 11.2 %

5. 56 – 65 5 2.4 %

Education Level

1. Primary or below 7 3.4 %

2. Secondary 82 40 %

3. College or above 116 56.6 %

4. Other 0 0 %

Occupation

1. Students 109 53.2 %

2. Homemaker 18 8.8 %

3. Employed 75 36.6 %

4. Retired 3 1.5 %

5. Other 0 0 %

Hours spend on smartphone applications per day

1. Less than 1 hour 26 12.7 %

2. 1 – 2 hours 49 23.9 %

3. 2 – 5 hours 75 36.6 %

4. 5 – 8 hours 31 15.1 %

5. More than 8 hours 24 11.7 %

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Appendix 5: Descriptive Statistics for Factors

Factors Number of Items Mean Std. Deviation Cronbach’s Alpha

For Both Mobile Payment Methods

PIIT 3 4.569 1.223 0.854

For Credit Card Mobile Payment

PE 4 4.880 1.366 0.948

EE 4 4.835 1.328 0.926

SI 2 3.954 1.304 0.909

FC 2 4.756 1.452 0.842

TR 2 4.366 1.432 0.825

CO 3 4.455 1.383 0.928

PR 4 5.067 1.608 0.748

BI 3 4.468 1.413 0.942

For Octopus Mobile Payment

PE 4 5.027 1.544 0.746

EE 4 4.841 1.338 0.940

SI 2 4.163 1.307 0.935

FC 2 4.790 1.355 0.831

TR 2 4.395 1.476 0.881

CO 3 4.569 1.377 0.928

PR 4 4.743 1.385 0.910

BI 3 4.641 1.429 0.951

54

Appendix 6: Results of reliability test

Construct Cronbach’s Alpha Items Cronbach’s Alpha

if item Deleted


Personal Innovativeness in

the Domain of Information

Technology

0.642 PIIT1 0.445

PIIT2 0.425

PIIT3* 0.854

PIIT4 0.383

For Credit Card Mobile Payment

Performance Expectancy 0.746 PE1 0.642

PE2 0.618

PE3 0.619

PE4 0.939

Effort Expectancy 0.940 EE1 0.922

EE2 0.915

EE3 0.913

EE4 0.938

Social Influence 0.935 SI1 N/A

SI2 N/A

Facilitating conditions 0.785 FC1 0.602

FC2 0.678

FC3* 0.831

Trialability 0.881 TR1 N/A

TR2 N/A

Communicability 0.706 CO1 0.457

CO2 0.477

CO3 0.484

CO4* 0.928

Perceived Risk 0.910 PR1 0.900

PR2 0.863

PR3 0.879

PR4 0.888

Behavioral Intention 0.951 BI1 0.937

BI2 0.916

BI3 0.932

(* Items deleted)

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Construct Cronbach’s Alpha Items Cronbach’s Alpha

if item Deleted


Performance Expectancy 0.746 PE1 0.642

PE2 0.618

PE3 0.619

PE4 0.939

Effort Expectancy 0.940 EE1 0.922

EE2 0.915

EE3 0.913

EE4 0.938

Social Influence 0.935 SI1 N/A

SI2 N/A

Facilitating conditions 0.785 FC1 0.602

FC2 0.678

FC3* 0.831

Trialability 0.881 TR1 N/A

TR2 N/A

Communicability 0.706 CO1 0.457

CO2 0.477

CO3 0.484

CO4* 0.928

Perceived Risk 0.910 PR1 0.900

PR2 0.863

PR3 0.879

PR4 0.888

Behavioral Intention 0.951 BI1 0.937

BI2 0.916

BI3 0.932

(* Items deleted)

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Appendix 7: Correlation of Constructs for Credit Card Mobile Payment

PE EE SI FC TR CO PR BI PIIT

PE 1

EE .736** 1

SI .367** .457** 1

FC .570** .714** .575** 1

TR .435** .674** .532** .734** 1

CO .460** .636** .447** .606** .642** 1

PR .339** .192** .049 .199** .090 .068 1

BI .593** .665** .423** .676** .635** .565** .277** 1

PIIT .374** .477** .410** .555** .529** .521** .085 .451** 1

**. Correlation is significant at the 0.01 level (1-tailed).

Appendix 8: Correlation of Constructs for Octopus Mobile Payment

PE EE SI FC TR CO PR BI PIIT

PE 1

EE .679** 1

SI .482** .562** 1

FC .628** .769** .621** 1

TR .528** .684** .588** .691** 1

CO .605** .783** .641** .733** .732** 1

PR .127* .134* -.030 .161* .060 .072 1

BI .585** .708** .534** .731** .666** .748** .145* 1

PIIT .413** .493** .359** .542** .506** .526** .097 .549** 1

**. Correlation is significant at the 0.01 level (1-tailed).

*. Correlation is significant at the 0.05 level (1-tailed).

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Appendix 9: UTAUT Model

Appendix 10: Adopter Group

Comparison of Consumers’ behavioral intention...

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