user adoption of ec and mc

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Comparison between User Adoption of Electronic Commerce and Mobile Commerce in Hong Kong

Wong Chun Yu (03007367)

Comparison between User Adoption of Electronic Commerce and

Mobile Commerce in Hong Kong

BY

Wong Chun Yu

03007367

Information Systems Management Option

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 2006


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Abstract

The objective of this project is to compare the adoption of Electronic Commerce

(E-commerce) and Mobile Commerce (M-commerce) in Hong Kong. This project determines

the importance of perceived risk in the context of transaction, perceived risk with

product/service, perceived ease of use, and perceived usefulness to purchasing behavior in

E-commerce and M-commerce environment. A research model was developed based on the

e-Commerce Adoption Model (e-CAM) proposed by Park, Lee and Ahn (2004). In this

project, analysis is based on 175 respondents having experience in using both E-commerce

and M-commerce. In addition, to understand the level of adoption of E-commerce of Hong

Kong compared with other countries and facilitate a better comparison of E-commerce and

M-commerce in Hong Kong, this project also studies the difference of the adoption of

E-commerce in USA, Korea and Hong Kong.

Construct validity is analyzed by confirmatory factor analysis. Reliability of constructs is

analyzed by Cronbach alpha test. Path analysis is used to assess the proposed model. The

result of path analysis shows that perceived risk in the context of transaction, perceived risk

with product/service, perceived ease of use, and perceived usefulness affect purchasing

behavior in E-commerce context. On the other hand, only perceived risk with product/service,

perceived ease of use, and perceived usefulness affect purchasing behavior in M-commerce

context.

These findings are important to E-commerce providers and M-commerce providers to

facilitate consumers adoption behavior of E-commerce and M-commerce in Hong Kong. The

differences between E-commerce and M-commerce can help these providers to improve their

business by enhancing the strengths and minimizing the weaknesses of E-commerce and

M-commerce.


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Acknowledgement

I would like to express my deepest gratitude to my BBA project supervisor, Dr. Vincent Chow

W. S. for this valuable advice and guidance throughout the research process.

Also, I would like to thank Mr. Dongwon Lee for sending me the original questionnaire of his

research.

Moreover, I would like to say thank you to all the respondents who have spent their valuable

time on answering my questionnaires. Besides, I must say thank you to my family and friends

who have given me a lot of support.


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Table of Contents

1. Introduction P.1-2

1.1 Background P.1

1.2 Objectives of This Study P.2

2. Literature Review P.3-13

2.1 Definition of E-commerce P.3-4

2.2 Definition of M-commerce P.4-5

2.3 Comparison between E-commerce and M-commerce P.5-7

2.4 e-Commerce Adoption Model (e-CAM) P.7-13

2.4.1 Original Technology Acceptance Model (TAM) P.8-9

2.4.2 Perceived Risk P.9-132.4.2.1 Perceived Risk with Product/ Service P.10-11

2.4.2.2 Perceived Risk in the Context of Online Transaction P.12-13

3. Research Model P.14-16

3.1 Statement of Hypotheses P.14-16

3.1.1 Perceived Risk in the context of Online Transaction P.14-15

3.1.2 Perceived Risk with Product/Service P.15-16

3.1.3 Perceived Ease of Use P.163.1.4 Perceived Usefulness P.16

4. Research Methodology P.17-19

4.1 Questionnaire Design P.17-18

4.2 Sample and Data Collection Procedures P.18

4.3 Data Analysis Method P.19

5. Analysis and Result P.20-29

5.1 Primary Data Analysis and Descriptive Statistics P.20-22

5.2 Confirmatory factor analysis P.22-23

5.3 Internal Consistency Reliability P.23-24

5.4 Path Analysis P.25-29

5.4.1 Direct Effects P.26-27

5.4.2 Indirect Effects P.27-28

5.4.3 Total Effects P.28-29

6. Discussion and Implications P.29-39


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6.1 Adoption of E-commerce in Hong Kong P.30-32

6.2 Adoption of M-commerce in Hong Kong P.33-35

6.3 Comparison between Adoption of E-commerce and

M-commerce in Hong Kong

P.35-36

6.4 Adoption of E-commerce in USA P.36-37

6.5 Adoption of E-commerce in Korea P.37-38

6.6 Comparison between Adoption of E-commerce in USA, Korea

and Hong Kong

P.38-39

7. Conclusion P.40-41

8. Limitations P.42

9. References P.43-50

10. Appendices P.51-96

Appendix A: Questionnaire P.51-58

Appendix B: Descriptive Data P.59-69

Appendix C: Internal Consistency Reliability Test Result P.70-75

Appendix D: Path Analysis P.76-82

Appendix E: Tables P.83-87Appendix F: Results of USA and Korea by Park, Lee and Ahn(2004) P.88-94

Appendix G: Confirmatory Factor Analysis P.95-96


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Wong Chun Yu (03007367) 1

1. Introduction

1.1 Background

The Internet has grown quickly after the emergence of the World-Wide Web in the early

1990s (Park, Lee and Ahn, 2004). It has changed consumers way to buy goods and services

from physical stores to electronic mode. Books, CDs, software, plane tickets, clothing and

groceries can be purchased on-line (McCloskey, 2004). Online shopping is a form of

Electronic Commerce (E-commerce) which can be defined as buying and selling of goods and

services on the Internet (Frolick and Chen, 2004). There were 946 million Internet users in the

world in 2004, it is expected the number of interest users will soar to 1460 million in 2007

(epaynews.com, 2005).

Mobile Commerce (M-commerce) refers to any transactions with a monetary value conducted

via a wireless telecommunication network (Wu and Wang, 2005). These transactions involve

intangible goods like applications and information delivered to the mobile device in electronic

format, and tangible goods that are acquired by using the mobile device but delivered to

customers separately (Nokia.co.uk, 2006). M-commerce also allows people to interact with

others wirelessly, anytime and anywhere using mobile phones, personal digital assistants

(PDA), laptop computers (Coursaris, Hassanein and Head, 2003). According to the study

conducted by Telecom Trends International, Inc. in 2003, there were 94.9 million

M-commerce users in 2003. This figure will jump to 1.67 billion users by 2008.


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1.2 Objectives of This Study

E-commerce and M-commerce are hot topics currently. Many previous researches have

examined the adoption of E-commerce (Bellman, Lohse, Johnson, 1999 Chen, Gillenson and

Sherrell, 2002 Gefen, Karahanna and Straub, 2003 McCloskey, 2003/2004 Klopping,

McKinney, 2004 Monsuwe, Dellaert and Ruyter, 2004) and the adoption of M-commerce

(Hung, Ku and Chung, 2003 Lu, Yu, Liu and Yao, 2003 Wu and Wang, 2005 Nysveen,

Pedersen and Thorbjornsen, 2005), but there was almost no researches studying the

differences between the adoption of E-commerce and M-commerce (Okazaki, 2005). The

differences are very important to E-commerce and M-commerce providers to determine the

strengths and weaknesses of E-commerce and M-commerce. Noticing the strengths and

weaknesses can help these providers to improve their business and formulate different

strategies. So, this project aims to examine the differences between adoption of E-commerce

and M-commerce in Hong Kong.

In order to understand the level of adoption of E-commerce of Hong Kong compared with

other countries and facilitate a better comparison of E-commerce and M-commerce in Hong

Kong, this project also compares the adoption of E-commerce in Hong Kong, USA and Korea.


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2. Literature Review

In this chapter, relevant literature about E-commerce and M-commerce and e-CAM are

reviewed and presented as follows: 2.1) Definition of E-commerce 2.2) Definition of

M-commerce 2.3) Comparison between E-commerce and M-commerce 2.4) e-Commerce

Adoption Model (e-CAM).

2.1 Definition of E-commerce

E-commerce can be defined as the ability of buying and selling products and information on

the Internet and other online services (Ngai and Wat, 2002). It facilitates open communication

and a virtual interactive environment in which vendors and customers can exchange products

and information (Gunasekaran, Ngai, 2005). Desktop computers are mainly used to conduct

wired E-commerce (Coursaris, Hassanein and Head, 2003) in fixed location (Ghosh and

Swaminatha, Feb 2001). HTML (Hyper-Text Markup Language) is widely adopted by the

Internet community as a format for browsing (Siau, Lim and Shen, 2001). HTTP is a protocol

that renders a communication standard between server computers and client over the Internet

(Hal, 1996).

Many previous researches have been undertaken in E-commerce. Table 1 shows the previous

researches on E-commerce.

Table 1: Previous researches on E-commerce

Research Literature

Acceptance of E-commerce Bellman, Lohse, Johnson (1999) Chen, Gillenson, Sherrell

(2002) Gefen, Karahanna and Straub (2003)

McCloskey(2003/2004), Klopping, McKinney (2004)

Monsuwe, Dellaert and Ruyter (2004) Shang, Chen and

Shen (2005)

Internet Banking/Finance Lau, Yen and Chau (2001), Harris and Spence (2002), Lai

and Li (2005)


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Internet Advertising/

Marketing

Kiani (1998)

Security Nugent, Raisinghani (2002)

Online Aunction Dans (2002)

Trust Hoffman, Novak and Peralta (1999), Jarvenpaa, Tractinsku

and Vitale (2000), Bryant and Colledge (2002), Yoon

(2002)

2.2 Definition of M-commerce

M-commerce refers to any transactions with a monetary value conducted via a wireless

telecommunication network (Wu and Wang, 2005). M-commerce is conducted through

various wireless devices like cell phones, personal digital assistants (PDA) and

wireless-enabled laptops (Coursaris, Hassanein and Head, 2003). These mobile devices allow

users to receive information and conduct transactions from virtually any location on a

real-time basis (Clarke III, 2001 Venkatesh, Ramesh and Massey, 2003). The basis for

information representation is WML (Wireless Markup Language) (Matskin and Tveit, 2001)

or compact HTML (cHTML) (Coursaris, Hassanein and Head, 2003). Wireless Application

Protocol (WAP) is a protocol specifically designed to transfer Web information to mobile

phones to enable them to access the Internet. With WAP, mobile phones become

communication devices that can communicate with other devices over a wireless network

(Siau, Lim and Shen, 2001).

M-commerce provides a wide range of services, for example, web information search, SMS

(Short Message Services), MMS (Multimedia Message Service), banking, gaming, chat,

weather forecast, etc (Okazaki, 2005).

Many previous researches have been undertaken in M-commerce. Table 2 shows the previous

researches on M-commerce.


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Table 2: Previous researches on M-commerce

Research Literature

Acceptance of M-commerce Hung, Ku and Chung (2003) Lu, Yu, Liu and Yao

(2003) Wu and Wang (2005) Nysveen, Pedersen and

Thorbjornsen (2005)

Mobile Finance/Banking Kleijnen, Wetzels and Ruyter (2004), Luarn and Lin

(2005)

Mobile Advertising Tsang, Ho and Liang (2004)

Cross-cultural issues in

M-commerce

Harris, Rettie and Kwan (2005)

Mobile gaming Kleijnen Ruyter, and Wetzels (2004)

2.3 Comparison between E-commerce and M-commerce

E-commerce and M-commerce environment and activities have many similarities because

both of them enable consumers to purchase products/services in a "virtual" environment

(Mobileinfo.com). Also, both of them represent a great opportunity for businesses to connect

to consumers (Venkatesh, Ramesh, Massey, 2003). The differences between E-commerce and

M-commerce are as follows:

a) Communication mode

E-commerce requires wired connection to a LAN but M-commerce is conducted through

wireless network. Wireless networks enable users to use M-commerce anytime and anywhere

(Coursaris, Hassanein and Head, 2003).

b) Devices

Desktop computers are mainly used to conduct wired E-commerce. On the contrary, wireless

devices like cell phones, personal digital assistants (PDA) and wireless-enabled laptops are

used to conduct M-commerce (Coursaris, Hassanein and Head, 2003). Desktop computers

provide large screen for conducting E-commerce, however, mobile devices only have small


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screens (Siau, Lim and Shen, 2001). In addition, mobile devices have less resource than

desktop devices including disk capacity, memory and computational power (Bermudez, 2002).

However, mobile devices are more portable (Siau, Lim and Shen, 2001).

c) Development languages and communication protocols

Hypertext markup language (HTML) is used to run wired World Wide Web. However, HTML

is not suitable to be used for exchanging information in mobile commerce. So, mobile devices,

on the other hand, run on one of two variations of HTML: wireless markup language (WML)

or compact HTML (cHTML).WML and cHTML are needed since mobile devices should

comply with communication protocols like Wireless Application Protocol (WAP) (Coursaris,

Hassanein and Head, 2003).

d) Enabling technologies

Technologies such as cookies, JAVA, active server pages can be compatible with E-commerce

on the web. However, these technologies are not compatible with WAP of M-commerce

(Coursaris, Hassanein and Head, 2003).

e) Fixed location vs. Ubiquity

E-commerce transactions are conducted by users in fixed location using workstations and

personal computers (Ghosh and Swaminatha, 2001). However, mobile devices allow users to

receive information and conduct transactions from virtually any location on a real-time basis

with a similar access level available through fixed-line technology (Clarke III, 2001

Venkatesh, Ramesh and Massey, 2003). In this sense, service or application are made

available through M-commerce wherever and whenever such need arises (Siau, Lim and Shen,

2001), for example, M-commerce users can perform time-critical activities like selling

declining stocks or getting driving directions while on vacation through the wireless network


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(Venkatesh, Ramesh, Massey, 2003).

f) Convenience

People are constrained by time or place in accessing E-commerce activities. However, a

mobile device can assist users immensely in daily life such as handling daily transactions and

carrying out internet-based activities through M-commerce applications when they are

waiting in line or stuck in traffic (Clarke III, 2001). Also, with the help of mobile devices,

mobile users can engage in activities like meeting people or traveling while receiving

information or doing transactions at the same time (Siau, Lim and Shen, 2001). Consumers

may realize the comfort brought by M-commerce which can in turn translate into improved

quality of life (Clarke III, 2001).

g) Personalization

Mobile devices are generally more personal in nature than desktop computers since the

former are more portable (Siau, Lim and Shen, 2001). In other words, users carry the mobile

device at most times (Coursaris, Hassanein and Head, 2003). Therefore, M-commerce

provides opportunities for individual-based target marketing (Clarke III, 2001). As owners of

mobile devices usually need different sets of services and application, M-commerce

applications can be personalized to provide information or services to meet the needs of

specific users (Siau, Lim and Shen, 2001).

2.4 e-Commerce Adoption Model (e-CAM)

In this research project, e-Commerce Adoption Model (e-CAM) is used. This model is

derived from the theoretical foundations of Technology Acceptance Model and the theories of

perceived risk. It examines the effect of the following factors on the actual use or purchasing

behavior of consumers in E-commerce: perceived ease of use, perceived usefulness, perceived


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risk with products/ services, and perceived risk in the context of online transaction. In the

following sections, the literature of Original Technology Acceptance Model (TAM) and

Perceived Risk are presented.

2.4.1 Original Technology Acceptance Model (TAM)

TAM was developed by Davis in 1989 to predict and explain user behavior and IT usage and

adoption. It was derived from Theory of reasoned action (TRA), it adopts TRA

belief-attitude-intention-behavior relationship to user acceptance of Information Technology

(Park, Lee and Ahn, 2004). TRA was developed by Fishbein and Ajzen in 1975. TRA

proposed that human behavioral intention is affected by attitude and subject norm, but it had

weakness in using abstract concepts like belief and evaluation as factors affecting attitude (Yu,

Ha, Choi and Rho, 2004).

The original TAM (shown in Figure 1) contained perceived ease of use (PEU), perceived

usefulness (PU), attitude toward using (ATU), behavioral intention to use (BI), and actual

usage (AU). PEU and PU are the key determinants for system use. ATU directly predicts

users BI which determines AU.

External

Variables

Perceived

Ease of Use

Perceived

Usefulness

Actual Usage

Figure 1. Technology Acceptance Model

Attitude

Towards Using

Behavioral

Intention to Use


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Perceived ease of use (PEU) is defined as the degree to which a person believes that using a

particular system would be free of effort (Davis, 1989). Perceived Usefulness (PU) refers to

the degree to which a person believes that using a particular system would enhance his or her

performance (Davis, 1989). Actual Usage (AU) refers to the frequency of using a particular

system and the approximate number of times the user uses the particular system in a given

period of time (Fishbein and Ajzen, 1975).

TAM was widely used by researchers in explaining and predicting users acceptance and

adoption of information technology like E-mail (Gefen and Straub, 1997), Spreadsheet and

Database Management package (Hendrickson, Massey, Cronan, 1993), personal computing

(Igbaria, Zinatelli, Cragg and Cavaye, 1997), digital library (Hong, Thong, Wong and Tam,

2002).These results shows that TAM has a significant power to predict and explain user

adoption of information system.

It has proven suitable to use TAM to examine the acceptance of E-commerce (Chen,

Gillenson and Sherrell, 2001 Gefen, Karahanna and Straub, 2003 Klopping, McKinney,

2004 Monsuwe, Dellaert and Ruyter, 2004) and M-commerce (Hung, Ku and Chung, 2003

Lu, Yu, Liu and Yao, 2003 Nysveen, Pedersen and Thorbjornsen, 2005 Wu and Wang, 2005).

2.4.2 Perceived Risk

After Bauer (1960) first advocated that consumer behavior was risk taking, there has been

several researches attempting to find out different types of perceived risk in the context of

consumers purchase behavior (Park, Lee and Ahn, 2004). Perceived risk is defined as the

subjective expectation of suffering a loss in pursuit of a desired outcome (Wang, Wang, Lin

and Tang, 2003). From marketing perspective, perceived risk is defined as the nature and

amount of risk perceived by a consumer in contemplating a particular purchase decision


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Wong Chun Yu (03007367) 10

(Cox and Rich, 1964). It is suggested that perceived risk is powerful at explaining consumers'

behavior because consumers strive more to avoid mistakes than to maximize utility in buying

(Mitchell, 1999). So and Sculli (2002) claimed that customers sometimes may not purchase

anything although they perceive a high value in product/service because they perceive a high

risk in such acquisition. Lee, McGoldrick, Keeling and Doherty (2003) suggested that risk

perception in consumers mind is a primary obstacle to the growth of E-commerce in the

future.

Theory of perceived risk can be applied in M-commerce too. There were researches taking

perceived risk into consideration in M-commerce (Lee, McGoldrick, Keeling and Doherty,

2003 Luarn and Lin, 2005 Wu and Wang, 2005).

Perceived risk in this project is subdivided into Perceived Risk with Product/ Service and

Perceived Risk in the Context of Online Transaction. These two types of risk are presented in

the following sections.

2.4.2.1 Perceived Risk with Product/ Service

Perceived risk with product/service has a lot a definition. Cox and Rich (1964) discussed the

element of risk including economic cost time loss (having to return the purchased goods,

delay in getting the needed item) ego loss and frustration (dissatisfaction caused by making

bad purchase decision) and failure to achieve buying goals. Roselius (1971) proposed four

types of losses related to risk, they were time loss (time, convenience and effort wasted to get

the failed products adjusted, repaired, or replaced) hazard loss (products are dangerous to

health when they fail) ego loss (feel foolish when the product bought is defective) money

loss (money spent on making the failed products to work properly or replacing them). Jacoby

and Kaplan (1972) identified five types of perceived risk: financial (the chances of losing


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Wong Chun Yu (03007367) 11

money if the product purchased does not work well), performance (the risk that the product

does not work properly), physical (the risk that the product is harmful to health),

psychological (the risk that the product will not fit with self-image), and social risk (the risk

that the product will affect the way others think of the buyer).

Laroche, Bergeron and Goutaland (2003) claimed that intangibility of product/service is

positively associated with perceived risk. It is because in the virtual context of online

purchasing, goods and services are intangible, so consumers will feel anxiety and have a

higher perceived risk (Ueltschy, Krampf and Yannopoulos, 2004). As a result, they will try to

avoid the risk by not using online purchasing.

In this project, Perceived Risk with Product/ Service only focuses on functional loss, financial

loss, time loss, opportunity loss and overall perceived risk with product/service. Definition of

each perceived risk type is based on Park, Lee and Ahn (2004) and is shown in Table 3.

Table 3: Definition of the Types of Perceived Risk with Product/Service

Risk Type Definition

Functional loss The risk that the product/service will not perform as

expected.

Time loss The risk of time spent on exchanging and returning the

product/service purchased when they fail.

Financial loss The risk of money spent on exchanging and returning

the product/service purchased when they fail.

Opportunity loss The risk that a product/service of equal or higher

quality at a lower price was found after purchasing.

Overall perceived risk

with product/service

The overall risk in product/service when purchasing.


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Wong Chun Yu (03007367) 12

2.4.2.2 Perceived Risk in the Context of Online Transaction

As a new type of business activity, internet shopping involves more uncertainty and risk than

traditional shopping. Consumers cannot monitor the safety and security of sending sensitive

personal and financial information (e.g., credit card numbers) through the Internet to a party

whose behaviors and motives may be difficult to predict (Lee and Turban, 2001). Coursaris,

Hassanein and Head (2003) expressed that the privacy and security concerns exhibited by

E-commerce consumers are also applicable to M-commerce consumers. Bermudez (2002) and

Coursaris, Hassanein and Head (2003) had the idea that M-commerce users are apprehensive

of divulging their credit card information and personal information on a network since the

security is still needed to be improved and consumers do not have much confidence in the

security of wireless infrastructure. Therefore, privacy and transaction security is also a barrier

to M-commerce (Wu and Wang, 2005).

Keen (1997) claimed that although the Internet is becoming more secure, people do not trust it

yet because they think Internet is not safe enough. Even though there are advances in Internet

security mechanisms such as SHTTP, cryptography, and authentication, consumers are still

concerned about using an impersonal transaction medium for secure transactions

(Swaminathan, Lepkowsha-White and Rao, 1999). Also, Hoffman, Novak and Peralta (1999)

argued that more people have yet to purchase online or provide personal information to web

providers in exchange for access to information because consumers do not trust most web

providers.

Lack of trust is one of the most important reasons for consumers not buying from Internet

shops (Lee and Turban, 2001). Trust enables people to take risk (McAllister, 1995). As trust

decreases, people are more unwilling to take risks and demand higher protections against the

probability of betrayal (Ratnasingham, 1998). One of the results of trust is that it decreases
http://hk.dictionary.yahoo.com/search.html?s=divulge


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Wong Chun Yu (03007367) 13

the perception of risk associated with opportunistic behaviors by the seller (Ganesan, 1994).

The lower the consumers perception of risk, the lower would be their perception of the

variance or uncertainty in the benefits derived (Bhatnagar, Misra and Rao, 2000). Otherwise,

consumers will be less willing to shop online if the online stores are not trustworthy

(Jarvenpaa, Tractinsky and Vitale, 2000).

Ratnasingham (1998) suggested that the basic requirements of secure electronic commerce

include authorization, authentication, integrity, confidentiality, availability, non-repudiation,

privacy. Bhimani (1996) claimed that electronic commerce security is under threats that could

manifest from illegal activities like eavesdropping, password sniffing, data modification,

spoofing and repudiation. Like E-commerce, M-commerce should address the security issue.

Frolick and Chen (2004) mentioned that wireless networks, like wired networks, must be

designed to provide the authentication, privacy, integrity, and non-repudiation necessary for

secure online transactions.

In this project, perceived risk in the context of online transaction includes privacy, security

(authentication), nonrepudiation and overall perceived risk in the context of transaction.


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Wong Chun Yu (03007367) 14

3. Research Model

The main objective of this project is to examine difference between the adoption of

E-commerce and M-commerce in Hong Kong. The research model for this project is adopted

from the e-Commerce Adoption Model (e-CAM) proposed by Park, Lee and Ahn (2004)

which is shown in Figure 2. As reviewed in Literature Review in Section 2, TAM and

Theories of Perceived Risk integrated in the e-CAM were used to explain the adoption

behavior of M-commerce. So, this model e-CAM is used to explain the adoption of both

E-commerce and M-commerce.

Perceived Risk in

the context of

Transaction (PRT)

H1

H3 H4

H2

H5

H6Perceived Risk with

Product/Service

(PRP)

Perceived Ease of

Use (PEU)

Perceived

Usefulness (PU)

Purchasing

Behavior (PB)

Figure 2. Research Model

In the following section, the relationships in the proposed model will be discussed and the

hypotheses will then be described

3.1 Statement of Hypotheses

3.1.1 Perceived Risk in the context of Online Transaction

Pavlou (2003) said that consumers perceived a high risk in the context of online transaction

because they have uncertainty about the theft of credit card information, breaches of private

information and stealing of personal information by hackers. Because of the risk, they are less

willing to use E-commerce.


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Wong Chun Yu (03007367) 15

Wu and Wang (2005) suggested that privacy and transaction security is a barrier to

M-commerce. It is because consumers think their personal information exchanged over the

wireless network is not safe (Coursaris, Hassanein and Head, 2003). So, the following

hypothesis is proposed:

H1: Perceived Risk in the Context of Online Transaction (PRT) negatively affects consumers

Purchasing Behavior (PB).

3.1.2 Perceived Risk with Product/Service

Due to the intangibility characteristics of products/services, consumers feel anxiety when they

use E-commerce (Park, Lee and Ahn, 2004 Ueltschy, Krampf and Yannopoulos, 2004). They

will be uncertain about the product/service quality, returns/exchanges policy, and price. These

anxiety and uncertainty made consumers to believe that there is a risk related to the

products/services, so they will purchase less using E-commerce.

Similarly, M-commerce users perceive risk since they cannot check and inspect the products

physically (Wu and Wang, 2005). This puts a barrier to the adoption of M-commerce. The

following hypothesis is proposed:

H2: Perceived Risk with Product/Service (PRP) negatively affects consumers Purchasing

Behavior (PB).

Since Sweeney, Soutar and Johnson (1999) and Lee, McGoldrick, Keeling and Doherty

(2003) claimed that each type of consumers risk is interdependent. Park, Lee and Ahn (2004)

assumed that perceived risk with products/services is correlated to perceived risk in the

context of online transaction. Therefore, the following hypothesis is proposed:


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Wong Chun Yu (03007367) 16

H3: Perceived Risk with Product/Service (PRP) is positively correlated with Perceived Risk

in the Context of Online Transaction (PRT).

3.1.3 Perceived Ease of Use

Perceived Ease of Use is defined as the degree to which a person believes that using a

particular system would be free of effort (Davis, 1989). If users find it easy to search and

locate information about goods/services and make an order, they will engage in E-commerce

and M-commerce more.

Davis (1989) found that Perceived Ease of Use has a significant effect on IT usage and it

indirectly affects usage via Perceived Usefulness. This is understandable that if users have

difficulties in using E-commerce and M-commerce, they have to spend a lot of time to deal

with them and find it less productive to use E-commerce and M-commerce. So, the following

hypotheses are proposed:

H4: Perceived ease of use (PEU) positively affects perceived usefulness (PU).

H5: Perceived ease of use (PEU) positively affects consumers purchasing behavior (PB).

3.1.4 Perceived Usefulness

Perceived Usefulness refers to the degree to which a person believes that using a particular

system would enhance his or her performance (Davis, 1989). When customers believes that

they can save time and money, buy a wide variety of goods/services over the Internet and

mobile phone, they will be more likely to purchase goods/services using E-commerce and

M-commerce. As a result, the following hypothesis is proposed:

H6: Perceived usefulness (PU) positively affects consumers purchasing behavior (PB).


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Wong Chun Yu (03007367) 17

4. Research Methodology

The research methodology is presented in this chapter. The questionnaire is in Appendix A.

This section is divided into 3 parts: 4.1) Questionnaire Design, 4.2) Sample and Data

Collection Procedures, and 4.3) Data Analysis Method.

4.1 Questionnaire Design

In this project, purchasing goods/services over the Internet and mobile phone were used to

examine the adoption of E-commerce and M-commerce in Hong Kong. Mobile phone was

used since it is believed that many Hong Kong people engaged in M-commerce by using it the

most. The questionnaire (See Appendix A) is divided into 4 parts. Part A includes screening

questions. Part B includes questions about factors affecting the adoption of E-commerce and

M-commerce (Perceived Ease of Use (Q.1-5), Perceived Usefulness (Q.6-10), Perceived Risk

with Product/Service (Q.11-15), and Perceived Risk in the context of Transaction (Q.16-19)).

In Part C, 9 items (Q.1-9) measures the E-commerce and M-commerce experience: 1) primary

connection system of Internet/ mobile phone, 2) place where Internet/ mobile phone was used

most, 3) Internet/ mobile phone experience, 4) frequency of using Internet/ mobile phone, 5)

hours of using Internet/ mobile phone per week, 6) amount spent on purchasing over Internet/

mobile phone during the past 6 months, 7) number of times of purchasing products/services

over the Internet/ mobile phone during the past 6 months, 8) purchased products/services, 9)

reasons for shopping over the Internet/ mobile phone. Part D is used to collect demographic

data like gender, age, education level, employment status and average monthly household

income.

Questions in Part A, B, C and D were adopted from Park, Lee and Ahn, 2004 to ensure

content validity. Question 1, 2 and 8 in Part C were modified to fit the M-commerce context.


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Question 3 and 5 in Part D were modified to fit Hong Kong environment.

All of the items in the questionnaire were on a Seven-point Likert Scale ranging from

strongly disagree to strongly agree. Items used in the questionnaire are all adopted from

literature of Park, Lee and Ahns e-CAM (Park, Lee and Ahn, 2004).

4.2 Sample and Data Collection Procedures

The data for this research project was collected from students and working population in

Hong Kong who have experience in both purchasing products/services over the Internet and

mobile phone at least once. The reason for choosing them as sample is that they are believed

to use Internet very often and have a mobile phone, so they have a greater chance to purchase

products/services over the Internet and mobile phone, and thus can provide a more objective

view of purchasing behavior. The screening questions in Part A filtered all respondents with

experience in purchasing over Internet and mobile phone, so all the questions in later parts are

based on their actual experience.

Paper-based questionnaire was used in data collection. They were distributed to my friends,

family members, colleagues, university students from 1st March 2006 to 1st April 2006. A total

of 250 people were invited to complete the questionnaire, 203 responses were received and

175 questionnaires were usable for analysis. It is because there were some missing data in 9

samples and 19 respondents have no experience in both/either purchasing over Internet and/or

mobile phone.


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4.3 Data Analysis Method

This section describes the statistical analysis techniques used in this project to test the

research model and associated hypothesis. SPSS v 13.0 will be used for data analysis like

primary data analysis and descriptive statistics, confirmatory factor analysis, internal

consistency reliability test and path analysis.

Primary Data Analysis and Descriptive Statistics display the frequencies and percent variables.

They are used for describing the demographic data Internet and mobile phone usage and

E-commerce and M-commerce experience. Section 5.1 displays the tables of the primary data

analysis and descriptive statistics. Confirmatory factor analysis (CFA) is used to test the

convergent validity of each construct in Section 5.2. Internal Consistency Reliability provides

the information about the degree to which the items are measuring the same construct.

Cronbachs alpha coefficient is used to measure internal consistency reliability in Section 5.3.

The acceptable level of Cronbachs alpha is larger than or equal to 0.7 (Nunally, 1978).

Higher alpha value implies higher reliability.

Path analysis in Section 5.4 will be used to assess the relationship between variables. It is an

application of multiple regression analysis to find out the direct effects and indirect effects of

independent variables on dependent variables. Dependent variable is affected by independent

variable whereas independent variable is not affected by any variables. P-value should be less

than 0.05 in order to prove that there is a relationship between independent variable and

dependent variable.


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5. Analysis and Result

The statistical results and analysis are presented in this chapter. SPSS data results are shown

in Appendix B, C and D. This section is divided into 4 parts: 5.1) Primary Data Analysis and

Descriptive Statistics, 5.2) Confirmatory factor analysis 5.3) Internal Consistency Reliability

and 5.4) Path Analysis.

5.1 Primary Data Analysis and Descriptive Statistics

The frequencies and percentages of gender, age, education level, employment status and

average monthly household income of the respondents are shown in Table 4. Internet usage

statistics, mobile phone usage statistics, online purchasing statistics and mobile phone

purchasing statistics are reported in Table 5, 6, 7 and 8 in Appendix E respectively.

Table 4: The frequencies and percentages of gender, age, education level,

employment status and average monthly household income of the respondents.Frequency Percent

Gender

Male 76 43.4

Female 99 56.6

Age

Under 16 2 1.1

16-25 152 86.9

26-35 11 6.336-45 10 5.7

46-55 0 0

Over 55 0 0

Education Level

Primary 0 0

Secondary (Form 1 Form 5) 13 7.4

Secondary (Form 6 Form 7) 16 9.1

Tertiary/ University 141 80.6

Postgraduate (Master Degree, PhD) 5 2.9


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Table 4 shows that the 43.4% of the respondents are male and 56.6% are female. Besides,

most of the respondents (86.9%) are between the ages of 16-25. Over 80% have university

education level. About two-third of the respondents are students. Nearly half of the

respondents have an average monthly household income less than $4,000.

From Table 5 in Appendix E, over 90% of Hong Kong people had a high Internet connection

speed faster than 56kb/sec. Over 80% of the respondents used the Internet at home, 9.1% at

school, and 9.1% at office and 0.6% at Internet Cafe. Over 90 % of the respondents had

Internet using experience more than 2 years and 65.1% use Internet more than twice a day.

From Table 6 in Appendix E, only 20% of the respondents used 3G. Over 50% of the

respondents used their mobile phone on street. Moreover, over 70% of the respondents have

used mobile phone for more than 2 years.

Employment Status

Full-time employed 39 22.3

Part-time employed 18 10.3

Self-employed 3 1.7

Student 114 65.1

Housewife 1 0.6

Unemployed 0 0

Retired 0 0

Others 0 0

Average monthly household income

Less than $4,000 85 48.6

$4,000-$7,499 16 9.1

$7,500-$9,999 12 6.9

$10,000-$14,999 25 14.3

$15,000-$19,999 16 9.1

$20,000-$49,999 18 10.3

$50,000-$100,000 3 1.7

Over $100,000 0 0


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From Table 7 in Appendix E, near one-fourth of the respondents have spent $200-$499 to

purchase goods/services online in the last 6 months. Also, over 75% of the respondents had

Internet purchasing experience at least once. The most popular item purchased from the

Internet was ticket (Air, Train, etc.). Convenience was the main reason for using online

purchasing.

From Table 8 in Appendix E, the majority of the respondents spent none on M-commerce in

the past 6 months. Around 60% have no mobile phone purchasing experience in last 6 months.

Near 40% used SMS and download ringtone using mobile phone. Many of them chose

convenience as the reason for using mobile phone for shopping.

5.2 Confirmatory factor analysis

The data collected were analyzed using principal component analysis as the extraction method

and Varimax as the rotation method. Since Question 2 and 8 were deleted in Park, Lee and

Ahns (2004) study after the factor analysis, for better comparison of the adoption of

E-commerce in Hong Kong, U.S.A and Korea in discussion part, Question 2 and 8 in the

questionnaire in this project were also deleted.

The factor analysis found that there were 5 factors with 19 scales loading in this study. Items

measuring the same construct/factor have a factor loadings higher than 0.6 for both

E-commerce and M-commerce. This represents that this questionnaire have satisfactory

validity. The result of factor analysis of Hong Kongs E-commerce and M-commerce is

shown in Table 9. The SPSS output is displayed in Appendix G.


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Table 9: Factor Analysis

Hong Kong

E-commerce M-commerce

PRT PU PEU PRP PB PRT PRP PEU PU PB

Ease of search -.12 .17 .82 -.01 .13 -.02 -.07 .73 .20 .22

Ease of ordering -.13 .24 .76 -.01 .22 .21 -.01 .74 .24 -.08

Customer service -.11 .19 .79 -.11 -.06 .11 .09 .81 .19 .12

Overall ease of use -.07 .27 .80 -.04 .18 .02 .06 .78 .28 .19

Save money -.26 .73 .27 -.02 -.00 -.07 .04 .09 .77 .04

Save time -.04 .83 .29 -.14 .07 .08 .05 .33 .70 .18

Variety of products -.06 .78 .18 -.02 .28 .10 .02 .32 .76 .14

Overall usefulness -.08 .86 .21 -.08 .19 .12 -.03 .24 .72 .10

Functional loss .5 .01 .01 .61 -.33 .28 .74 -.07 -.10 -.10

Time loss .24 -.13 .05 .81 -.11 .30 .71 -.06 .04 -.09

Financial loss .40 -.35 -.01 .69 .09 .21 .79 .09 -.05 .03

Opportunity loss .07 .09 -.16 .82 -.11 .15 .64 .14 .23 -.26

Overall PRP .60 -.12 -.09 .61 -.14 .49 .62 -.05 .04 .07

Privacy .81 .05 -.07 .18 -.27 .77 .27 .11 .01 .11

Security (Credit card) .82 -.23 -.07 .14 -.13 .85 .24 .00 .06 -.05

Non-repudiation .74 -.08 -.27 .28 .04 .79 .27 .17 .04 -.06

Overall PRT .81 -.17 -.13 .22 -.17 .77 .33 .09 .09 -.14Purchasing Times -.21 .23 .19 -.19 .81 -.09 -.08 .16 .21 .82

Purchasing Amount -.31 .29 .25 -.14 .75 .00 -.14 .20 .14 .82

* Extraction Method: Principal Component Analysis (Varimax Rotation with Kaiser Normalization)

^ PEU: Perceived Ease of Use PU: Perceived Usefulness PRP: Perceived Risk with Product/Service

PRT: Perceived Risk in the Context of Transaction PB: Purchasing Behavior

5.3 Internal Consistency Reliability

The Cronbachs Alpha of each variable along with mean and standard deviation of each scale

item of both E-commerce and M-commerce is shown in Table 10 and SPSS data result is

shown in Appendix C. The Cronbachs Alpha values ranged from 0.789 to 0.879 for

E-commerce and 0.708 to 0.875 for M-commerce. These results reflect that all scales for all

variables are satisfactory since the acceptance level of Cronbachs Alpha is larger than or

equal to 0.7 (Nunally, 1978).


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Table 10: Cronbachs Alpha, mean and SD

Hong Kong

E-commerce M-commerce

Factors and scale Items Mean /

SD

Cronbach

Alpha

Mean /

SD

Cronbach

Alpha

Perceived Ease of Use * .862 .827

Easy to search and locate desired

information

4.48/1.30 3.58/1.22

Easy to use from any location at any time 4.88/1.55 4.06/1.40

Easy to use the customer service 4.19/1.30 3.79/1.20

Overall PEU 4.58/1.37 3.80/1.26

Perceived Usefulness * .879 .802

Save money 4.19/1.40 3.47/1.14

Save time 5.21/1.52 3.99/1.26

Provide wide variety of products/services 5.04/1.40 3.91/1.14

Overall PU 4.75/1.45 3.99/1.15

Perceived Risk with Products/Services * .872 .821

Functional loss 5.02/1.13 4.73/1.26

Time loss 5.09/1.27 4.83/1.23

Financial loss 4.93/1.13 4.55/1.17

Opportunity loss 4.65/1.05 4.49/1.04Overall PRP 4.97/1.17 4.85/1.15

Perceived Risk in the Context of

Transaction *

.877 .875

Privacy 5.22/1.33 4.80/1.17

Security (Credit card) 5.44/1.21 5.04/1.29

Non-repudiation 4.97/1.37 4.97/1.20

Overall PRT 5.07/1.38 4.94/1.27

Purchasing Behavior .789 .708Total Amount of Online Purchasing ** 4.2/2.17 2.13/1.60

Frequency of Online Purchasing *** 2.89/1.43 1.87/1.27

*: 7-point scales ranging from strongly disagree to strongly agree.

**: 8-point scales ranging from none to more than $2,000.

***: 5-point scales ranging from none to more than 10 times.


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5.4 Path Analysis

In order to test the relationship of constructs (Perceived Risk in the context of Transaction

(PRT), Perceived Risk with Product/Service (PRP), Perceived Ease of Use (PEU), Perceived

Usefulness (PU), and Purchasing Behavior (PB)) in the proposed model shown in Chapter 3,

path analysis is used. Figure 3 and 4 shows the result of regression analysis of E-commerce

and M-commerce of this project. The direct effect, indirect effect and total effect among

dependent variable and independent variables of E-commerce and M-commerce in Hong

Kong are reported in Table 11, 12 and 13 respectively. The SPSS output is displayed in

Appendix D.

Perceived Risk in

the context of

Transaction (PRT)

-0.231** (H1)

0.657*** (H3) 0.539*** (H4)

-0.172* (H2)

0.185* (H5)

0.268*** (H6)Perceived Risk with

Product/Service

(PRP)

Perceived Ease of

Use (PEU)

PerceivedUsefulness (PU)

Purchasing

Behavior (PB)

Figure 3. Research Model Result (Hong Kong : E-commerce)

** *p


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5.4.1 Direct Effects

The results of direct effects were generated by using regression analysis and shown in Table

11. The results of hypothesized relationship are discussed below. The SPSS output is

displayed in Appendix D.

5.4.1.1 Direct Effect on Purchasing Behavior

Hypothesis 1, 2, 5 and 6 examine the direct effects of Perceived Risk in the context of

Transaction, Perceived Risk with Product/Service, Perceived Ease of Use and Perceived

Usefulness on Purchasing Behavior respectively. Table 11 displays the direct effects of these

hypotheses of E-commerce and M-commerce in Hong Kong.

For E-commerce in Hong Kong, Perceived Risk in the context of Transaction has a significant

direct effect on Purchasing Behavior at (= -0.231, p


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As a whole, Perceived Risk with Product/Service, Perceived Ease of Use and Perceived

Usefulness have a significant direct effect on Purchasing Behavior for both E-commerce and

M-commerce. Perceived Risk in the context of Transaction significantly affects Purchasing

Behavior in E-commerce but not M-commerce.

Table 11: Direct Effects

Direct Effect ()

HK: E-commerce HK: M-commerce

^Dependent

^Independent PU PB PU PB

PRT --- -0.231**(H1) --- -0.045(H1)

PRP --- -0.172*(H2) --- -0.211*(H2)

PEU 0.539***(H4) 0.185*(H5) 0.579***(H4) 0.242**(H5)

PU --- 0.268***(H6) --- 0.244**(H6)

R2=0.29 R2=.0401 R2=0.335 R2=0.223

^ PEU: Perceived Ease of Use PU: Perceived Usefulness PRP: Perceived Risk with

Product/Service PRT: Perceived Risk in the Context of Transaction PB: Purchasing Behavior

***p


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Table 12: Indirect Effects

Indirect Effect ()


^Dependent

^Path PB PB

PEU-PU-PB (0.539*0.268)=0.144 (0.579*0.244)=0.141



As shown in Table 12, Perceived Ease of Use has an indirect effect on Purchasing Behavior

via Perceived Usefulness ( = 0.144) in E-commerce of Hong Kong. For M-commerce in Hong

Kong, Perceived Ease of Use has an indirect effect on Purchasing Behavior via Perceived

Usefulness ( = 0.141) too.

5.4.3 Total Effects

Table 13 tells us the total effect on dependent variable (Purchasing Behavior) from

independent variables (Perceived Risk in the Context of Transaction, Perceived Risk with

Products/Services, Perceived Ease of Use, and Perceived Usefulness).

Table 13: Total Effects


PB PB

^Dependent

^Independent

Direct Indirect Total () Direct Indirect Total ()

PRT -0.231** -0.231 -0.045 -0.045

PRP -0.172* -0.172 -0.211* -0.211

PEU 0.185* 0.144 0.329 0.242** 0.141 0.383

PU 0.268*** 0.268 0.244** 0.244




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All results from hypothesis testing are summarized in Table 14.

Table 14: Hypotheses Testing Results

HK:

E-commerce

HK:

M-commerce

Hypothesis Casual Relationship P1 Result P1 Result

H1 PRT PB (-) .005 Supported .618 Rejected

H2 PRP PB (-) .032 Supported .019 Supported

H3 PRT PRP (+) .000 Supported .000 Supported

H4 PEU PU (+) .000 Supported .000 Supported

H5 PEU PB (+) .011 Supported .005 Supported

H6 PU PB (+) .000 Supported .004 Supported

1Statistical Significance of the Test

* PEU: Perceived Ease of Use PU: Perceived Usefulness PRP: Perceived Risk with


6. Discussion and Implications

The purpose of this project is to examine the difference between adoption of E-commerce and

M-commerce in Hong Kong. Factors (Perceived Ease of Use, Perceived Usefulness,

Perceived Risk with Products/Services, Perceived Risk in the Context of Transaction)

affecting the adoption of both E-commerce and M-commerce were investigated. In order to

have a better understanding of E-commerce in Hong Kong, research results by Park, Lee and

Ahn regarding the adoption of E-commerce in USA and Korea will be discussed too.

This discussion is divided into 6 main parts, 1) adoption of E-commerce in Hong Kong 2)

adoption of M-commerce in Hong Kong 3) comparison between adoption of E-commerce

and M-commerce in Hong Kong 4) adoption of E-commerce in USA 5) adoption of

E-commerce in Korea 6) comparison between adoption of E-commerce in USA, Korea and

Hong Kong. Appendix F indicates all the analysis and research results like confirmatory


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factor analysis, reliability analysis, mean, standard deviation, path analysis of USA and Korea

conducted by Park, Lee and Ahn (2004).

6.1 Adoption of E-commerce in Hong Kong

6.1.1 Influence on Purchasing Behavior

Perceived Usefulness, Perceived Ease of Use, Perceived Risk with Product/Service, and

Perceived Risk in the Context of Transaction have significant direct effects on Purchasing

Behavior in E-commerce in Hong Kong.

Perceived Usefulness was found to have a significant direct effect on Purchasing Behavior

which is consistent to previous researches (Davis, 1989 McCloskey, 2003/2004). As Hong

Kong people have a fast pace of life and long working hours, time is very crucial. In their

mind, time is money. When people can buy goods/services on online stores within a short

period of time without wasting too much time to shop at physical retail stores, they will use

E-commerce more.

Perceived Ease of Use significantly affects Purchasing Behavior which is consistent to

previous researches (Davis, 1989 McCloskey, 2003/2004). Therefore, the higher the level of

Perceived Ease of Use, the higher the E-commerce usage is. If users think that the website is

easy to navigate, and they can make an order easily, they will buy more using E-commerce.

Also, with the 24-hour Internet access, people can easily acquire goods/ service over the

Internet at anytime they want. Ease to use is very important to Hong Kong people since they

always have a busy life.

Perceived Risk with Products/Services is also an important factor affecting the adoption of

E-commerce which is consistent to previous research (Turban, Lee, King and Chung, 2000


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Kleijnen Ruyter, and Wetzels, 2004). It is believed that Chinese people are quite conservative,

they doubt whether they can really purchase a product from the virtual community-Internet

and do not rest assured that the quality of products purchased is good without physically

touching and checking them. As a result, their adoption rate of E-commerce will be lower.

Perceived Risk in the Context of Transaction poses a barrier to the adoption of E-commerce

which is consistent to previous research (Pitkow and Kehoe, 1996 Hoffman, Novak, Peralta,

1999 Rose, Khoo and Straub, 1999 Turban, Lee, King and Chung, 2000 McCloskey,

2003/2004). Security and privacy are the major concerns of Chinese consumers. Chinese

people are risk-aversive, they worry that their personal information and credit card

information will be manipulated by unauthorized parties. Unexpected increase of credit card

expense may be resulted. Also, in recent years, the negative effect of spyware further

increased the perception of risk in consumers mind. Moreover, respondents thought that

option for recourse is very limited for E-commerce. As a result, people will lower the usage of

E-commerce in order to avoid the risk. Online vendors and credit card companies could

strengthen encryption methods, and security protocols to guard against misuse of sensitive

and private information.

6.1.2 Influence on Perceived Usefulness

Perceived Ease of Use has a direct effect on Perceived Usefulness as shown in Table 11. This

is supported by previous researches (Davis, 1989 Lucas and Spitler, 1999 Venkatesh and

Davis, 2000 McCloskey, 2003/2004 Wang, Wang, Lin and Tang, 2003). This is

understandable that if users find it easy to use online shopping websites, they can make

decision faster and better since they do not have to spend a lot of time and effort to deal with

them.


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It implies that improving easiness of using the online shopping websites will take the

advantage of the indirect effect to Purchasing Behavior via Perceived Usefulness. Because of

this indirect effect, Perceived Ease of Use has the strongest total effect on Purchasing

Behavior as shown in Table 13.

6.1.3 Correlation of Perceived Risk in the Context of Transaction and Perceived Risk

with Product/Service

Perceived Risk in the Context of Transaction and Perceived Risk with Product/Service are

found to be interrelated which is consistent to previous research (Sweeney, Soutar and

Johnson 1999 and Lee, McGoldrick, Keeling and Doherty, 2003). When users perceive that

their option for recourse is limited in E-commerce, they will also think that they have to spend

more time and money to exchange or return the goods to the online vendors. So, online

vendors could pay more attention to both risks together instead of either one only.

6.1.4 Purchasing Behavior

From the research findings in Table 7 in Appendix E, over 75% of the respondents had

Internet purchasing experience at least once in the previous 6 months. However, they did not

use E-commerce to purchase very expensive goods/services since most of the respondents

(around 70%) spent below $500 in total in the last 6 months. They usually purchased tickets

like train and film ticket from the Internet.


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6.2 Adoption of M-commerce in Hong Kong

Perceived Usefulness, Perceived Ease of Use, and Perceived Risk with Products/Services

have significant direct effects on Purchasing Behavior in M-commerce context in Hong Kong.


Perceived usefulness has a significant positive relationship with Purchasing Behavior which

matches the result of previous researches (Davis, 1989 McCloskey, 2003/2004). If

M-commerce is very useful to users, they will use it more often. Hong Kong people are very

busy at work, if they find that M-commerce helps them to save much time and money from

making orders, they will use it more.

Perceived Ease of Use is an important factor affecting the Purchasing Behavior which is

consistent with previous researches (Davis, 1989 McCloskey, 2003/2004). Because of the

wireless nature and 24-hour access of M-commerce, people can easily purchase

goods/services anywhere at anytime. This matches the fact that many respondents chose

convenience as the reason for purchasing over mobile phone in my questionnaire. Also, since

many respondents use mobile phone on street, it is impossible for them to deal with difficult

purchase procedures on mobile phone within a short period of idle time. Therefore, if

M-commerce is easy to use, the consumers adoption rate will be higher.

Perceived Risk with Product/Service has a negative effect on the adoption behavior which

matches the previous studies (Turban, Lee, King and Chung, 2000 Kleijnen Ruyter, and

Wetzels, 2004). Hong Kong people are risk-aversive because of the Chinese culture. Due to

the wireless characteristics of M-commerce, interference and disconnection of network

reception may hinder the delivery and quality of services, so Hong Kong people do not dare

to acquire services using mobile phone.


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Perceived Risk in the context of transaction has an insignificant effect on Purchasing Behavior.

Despite the respondents concerned with privacy, security as shown in Table 10, but they are

not the barriers to the adoption of M-commerce. Since in respondents mind, purchasing

goods/services via mobile phone does not necessarily involve sending personal information

and credit card information over the wireless network.

6.2.2 Influence on Perceived Usefulness

Perceived Ease of Use has a direct effect on Perceived Usefulness as shown in Table 11. This

is supported by previous researches (Davis, 1989 Lucas and Spitler, 1999 Venkatesh and

Davis, 2000 McCloskey, 2003/2004 Wang, Lin and Tang, 2003). If users find it easy to

purchase over mobile phone, they can make decision faster and better since they do not have

to spend a lot of time and effort to deal with complicated buying procedures.

It implies that improving easiness of buying over mobile phone will take the advantage of the

indirect effect to Purchasing Behavior via Perceived Usefulness. Because of this indirect

effect, Perceived Ease of Use has the strongest total effect on Purchasing Behavior as shown

in Table 13.

6.2.3 Correlation of Perceived Risk in the Context of Transaction and Perceived Risk

with Product/Service

Perceived Risk in the Context of Transaction and Perceived Risk with Product/Service are

found to be interrelated as supported by Sweeney, Soutar and Johnson (1999) and Lee,

McGoldrick, Keeling and Doherty (2003). Since respondents may think that the

products/services purchased from M-commerce are intangible, so they will perceive that their

option for recourse is limited.


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The research findings from Table 8 in Appendix E shows that the majority of the respondents

spent none on M-commerce and near 60% had no mobile phone purchasing experience in the

past 6 months. In other words, people did not usually acquire goods/services using mobile

phone. For those who used mobile phone for purchasing, they usually spent below $50 in total

for the past 6 months. It is because money was mostly spent on SMS and ringtone which are

cheap. SMS and ringtone are the most popular services acquired from M-commerce in Hong

Kong which matches the results of Harris, Rettie and Kwan (2005).

6.3 Comparison between Adoption of E-commerce and M-commerce in

Hong Kong

The survey results showed that there were a few difference between E-commerce and

M-commerce.


Firstly, it indicates that the usage of Internet shopping is affected by privacy and security, but

that of mobile phone shopping is not. It may be due to the fact that consumers have to enter

their personal information and credit card information to purchase goods/services over the

Internet. On the contrary, they are not normally required to enter these information for

acquisition over mobile phone in Hong Kong. Although respondents perceived some level of

risk regarding security and privacy as shown in Table 10, their M-commerce behavior is not

affected since no private information and credit card information is needed for acquiring SMS

and ringtone. To acquire these services via mobile phone, consumers simply press a few

buttons and the expenses will be included in the monthly bill of mobile phone network service.

So, it is expected that usage of M-commerce will be affected by privacy and security if

personal digital assistants (PDA) and wireless-enabled laptops are included in the


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questionnaire survey. Because it is more often to enter personal information and credit card

information to purchase goods/services wirelessly when using these devices.


More respondents used E-commerce more than M-commerce and they usually spent more on

E-commerce than M-commerce. Many respondents answered that E-commerce provides a

larger variety of goods/services than M-commerce (refer to Table 7 and Table 8 in Appendix

E). So, it can be interpreted that the range of goods/services choices provided by

M-commerce providers in Hong Kong are relatively limited. Also, the respondents usually

purchased air, train and film tickets while M-commerce users acquired ringtone and SMS. We

can see that they generally acquired more expensive goods/services using E-commerce.

6.4 Adoption of E-commerce in USA

The result of adoption of E-commerce in USA comes from the research conducted by Park,

Lee and Ahn (2004) (Refer to Appendix F). Their result was based on Structural Equation

Model (SEM). Perceived Usefulness, Perceived Risk in the Context of Transaction, and

Perceived Risk with Products/Services significantly affect Purchasing Behavior in USA.


Consistent with Davis (1989) and McCloskey (2003/2004), Perceived Usefulness

significantly affects Purchasing Behavior. Americans engaged in E-commerce very often. It is

because E-commerce is useful to them. It enables them to acquire goods/services without

going outside. Since USA is a large country, people have to travel a long distance to shopping

mall, supermarkets. So, E-commerce helps them to save much traveling time and expenses.

Although Perceived Ease of Use does not have a direct effect on Purchasing Behavior which


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is the same as previous research (Szajna and Bernadette, 1996), it has an indirect effect on

Purchasing Behavior via Perceived Usefulness. When users find E-commerce easy to use,

decision making process will be faster since less time is wasted on using complicated web

pages. So, usage of E-commerce will soar.

Perceived Risk in the context of Transaction hinders users to shop online significantly which

is consistent with (Pitkow and Kehoe, 1996 Hoffman, Novak, Peralta, 1999 Rose, Khoo and

Straub, 1999 Turban, Lee, King and Chung, 2000 McCloskey, 2003/2004). Privacy and

security are their main concern. They are afraid that their personal information and credit card

information will be disclosed and manipulated by unauthorized parties, so they will be less

prone to E-commerce. Individualist culture of America can explain this. Americans have to

bear the consequences of their own decision even though they lose lots of money after making

a risky decision, no other people will help them (Park and Jun, 2003).

Consistent with Turban, Lee, King and Chung, (2000) and Kleijnen Ruyter, and Wetzels

(2004), Perceived Risk with Products/Services sets a barrier to adoption of E-commerce.

Americans are not sure the quality and functions of the goods on online stores since they

cannot directly see or touch the goods. Also, they do not want to take the risk to exchange or

return the products which are defective or do not function properly.

6.5 Adoption of E-commerce in Korea

The result of adoption of E-commerce in Korea comes from the research conducted by Park,

Lee and Ahn (2004) (Refer to Appendix F). Their result was based on Structural Equation

Model (SEM). Only Perceived Ease of Use has significant direct effect on Purchasing

Behavior.


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Perceived Ease of Use has significant direct effects on Purchasing Behavior which is

consistent with the research results of Davis (1989) and McCloskey (2003/2004). Since

E-commerce is not very popular in Korea, Koreans may not be familiar with the procedures

and operation of E-commerce. So, easy to learn and use is the first step of the expansion of

E-commerce.

Perceived Usefulness does not have significant effects on the adoption of E-commerce which

is supported by Huang (2005). The lack of online purchasing experience may be the reason

for this. Without prior experience, Koreans may not realize the usefulness of online shopping

like saving time and money.

Perceived Risk with Products/Services, and Perceived Risk in the Context of Transaction also

do not have significant direct effects on Purchasing Behavior. Although Koreans have some

concerns for Perceived Risk with Products/Services and Perceived Risk in the Context of

Transaction, these factors do not affect Purchasing Behavior. Because Koreans usually

purchase goods from large, well-known online stores where they feel more secure. So, risk is

minimized and is not an important factor affecting their online shopping behavior (Park and

Jun, 2003).

6.6 Comparison between adoption of E-commerce in USA, Korea and Hong

Kong


Regarding the factors affecting the usage of E-commerce in these three countries, Perceived

Risk in the context of Transaction and Perceived Risk with Products/Services significantly

affect Purchasing Behavior in USA and HK, but not Korea. It can be explained by cultural


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difference in attitude toward risk.

Perceived Usefulness is a significant factor affecting usage of E-commerce in USA and HK,

but not Korea. It is because E-commerce may be more mature and better developed in USA

and HK, it provides a lot of benefits to customers like saving time and money. On the other

hand, E-commerce may be in the developing stage in Korea, not many users realized its

advantages.

Perceived ease of use has a significant effect on E-commerce in HK and Korea but not USA.

This can be explained by the fact that Americans are used to E-commerce. They found no

difficulty in using online purchasing websites for making orders. So, whether online

purchasing is complicated or not does not affect the online purchasing amount and frequency

in USA.

The findings of Perceived Usefulness and Perceived Ease of Use on Purchasing Behavior are

consistent with the research conducted by McCloskey (2003/2004): participation of

E-commerce is based on Perceived Ease of Use (for Korea) while continued usage is based on

Perceived Usefulness (for USA).

As a whole, the adoption of E-commerce in USA, Korea and Hong Kong is different as

reflected by the purchasing behavior. The adoption rate is the highest in USA, followed by

Hong Kong, and then Korea. The discrepancies are mainly caused by cultural difference and

the difference in IT development.


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7. Conclusion

The proposed research model was based on Park, Lee and Ahns e-Commerce Adoption

Model (e-CAM), the main objective is to examine the differences between the adoption of

E-commerce and M-commerce in Hong Kong. Also, this project also studies the adoption of

E-commerce in Hong Kong, USA and Korea.

The result shows that all four independent variables including Perceived Ease of Use,

Perceived Usefulness, Perceived Risk with Products/Services, and Perceived Risk in the

Context of Transaction affect Purchasing Behavior in E-commerce context in Hong Kong. On

the other hand, only Perceived Ease of Use, Perceived Usefulness, and Perceived Risk with

Products/Services affect Purchasing Behavior in M-commerce context in Hong Kong.

This project provides useful insights for the E-commerce and M-commerce providers. It is

suggested that they should improve the ease of use by building user-friendly interface so that

consumers can easily and conveniently purchase the products/services. Perceived usefulness

is also important factor affecting consumers purchase decision because of fast pace of Hong

Kong. Therefore, saving time and money, providing a larger variety of goods/services are

essential. Moreover, perceived risk with product/service lowers the usage of E-commerce and

M-commerce. So, E-commerce and M-commerce vendors should reduce the anxiety of

customers by providing more detailed information about the product quality, returns or

exchanges policy, etc. Perceived Risk in the Context of Transaction impedes the growth of

E-commerce only since customers concern about the security and privacy of online

transactions. Online vendors should strengthen the security (through encryption,

authentication) of online transactions and should not disclose the private information of the

customers without their consensus. As a result, customers will trust E-commerce more and


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will not hesitate to purchase goods/services over the Internet.

As a whole, respondents use E-commerce more than M-commerce. Although M-commerce is

still an infant, it is growing very fast because of its unique anytime and anywhere advantage.

M-commerce providers should strengthen these inherent advantages to extend the use and

application of M-commerce. In the future, with the advancement of third generation (3G) and

fourth generation (4G), people can enjoy more benefits from M-commerce, like more

information and functions, faster download speed, higher mobility, etc. In my opinion, if

M-commerce performs similar functions like providing a large variety of goods/services and

having a faster download speed as E-commerce, the former will be a good alternative to the

latter.

The adoption of E-commerce in USA, Korea and Hong Kong is different. The adoption rate is

the highest in USA, followed by Hong Kong, and then Korea. The discrepancies are caused

by cultural difference and the difference in IT development. Compared with USA,

E-commerce can not be considered as a usual shopping method in Hong Kong. So,

E-commerce providers can promote more to Hong Kong citizens.


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8. Limitations

This research project has several limitations. Firstly, the proposed model e-CAM explained

about 40.1% of the total variance of consumers E-commerce adoption behavior and 22.3% of

total variance of consumers M-commerce adoption behavior in Hong Kong only. Further

research should include some other factors affecting the purchase behavior, like perceived

enjoyment and cost. Secondly, the samples were mainly students representing low-income

group and there were only 175 samples. So, this project cannot reflect the general adoption

behavior of E-commerce and M-commerce in Hong Kong. It is better to enlarge the sample

size, select sample randomly and include a more diverse sample from different social status,

age and income level in further research. In addition, for the questionnaire, only mobile phone

was used to measure M-commerce acceptance. Further research should include other wireless

devices like personal digital assistants (PDA) and wireless-enabled laptops. Lastly, this

project does not take the temporal factor into consideration. The E-commerce research of

USA, Korea by Park, Lee and Ahn was conducted in year 2004 whereas this project is

conducted in year 2006. The comparison of E-commerce adoption of USA, Korea and Hong

Kong may not reflect the true situation due to this temporal difference.


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