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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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Comparison between User Adoption of Electronic Commerce and Mobile Commerce in Hong Kong
Wong Chun Yu (03007367)
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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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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(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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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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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
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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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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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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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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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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 ()
HK: E-commerce HK: M-commerce
^Dependent
^Path PB PB
PEU-PU-PB (0.539*0.268)=0.144 (0.579*0.244)=0.141
^ 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
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
HK: E-commerce HK: M-commerce
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
^ 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
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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
Product/Service PRT: Perceived Risk in the Context of Transaction PB: Purchasing Behavior
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.
6.2.1 Influence on Purchasing Behavior
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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6.2.4 Purchasing Behavior
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.
6.3.1 Influence on Purchasing Behavior
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.
6.3.2 Purchasing Behavior
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.
6.4.1 Influence on Purchasing Behavior
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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6.5.1 Influence on Purchasing Behavior
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
6.6.1 Influence on Purchasing Behavior
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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