BY
School of Business in Partial Fulfilment
of the Graduation Requirement for the Degree of
Bachelor of Business Administration (Honours)
Hong Kong Baptist University
2
Acknowledgement
We would like to take the opportunity to express sincere gratitude to our
supervisor, Dr. Tony Wong, for his support during the research process. He devoted
his precious time and effort in giving us guidance and advice throughout the project.
With his kindly help, this study could be completed.
Besides, we would like to express special thanks to those who had helped us to
forward or distribute our questionnaires to those who are the target person in the study.
Without their help, we would not capture a certain amount of questionnaires in a short
period of time. Finally, we would like to thank all respondents who spent their
valuable time on completing the questionnaire.
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Abstract
This research examines previous Technology Acceptance Model (TAM)-related
studies in order to provide an expanded model that can further explain other factors
affecting web-user to shop online.
Amoroso and Hunsinger (2009) research has studied consumers’ acceptance of
online purchasing by extending original TAM, focusing on student consumers aged
between 18 – 22 in the United States and Australia. This research used some
constructs of their research and targeted web-users who have ever used and conducted
online shopping website without limitation of age and educational level in Hong Kong.
Internet-based questionnaires were distributed and 327 usable questionnaires were
collected. Our findings support our hypothesizes and model which can be a predictor
of web-users’ online shopping behaviors.
Different from Amoroso and Hunsinger (2009) research, this study takes into
account factors including External Variables, Attitude toward using, Perceived
Behavioral Control, Behavioral Intention, Social Influence, Voluntariness, Facilitating
Conditions affecting web-users to shop online.
Different factors have different moderating effect on Attitude toward using,
Behavioral Intention and these factors affecting customers’ future usage of online
shopping website. The finding shows that the relationship between Behavioral
Intention and Future Usage, relationship between External Variables and Attitude
toward Using and relationship between Attitude toward Using and Behavioral
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Intention are significant in all hypothesizes in which Behavioral Intention is the most
significant among these variables.
The result also finds that relationship between Social Influence and Behavioral
Intention, relationship between Facilitating and Future Usage, relationship between
Perceived Behavioral Control and Behavioral Intention are non-significant in all
hypothesizes in which Social Influence is the most non-significant among these
variables. The other relationships including Perceived Behavioral Control and Future
Usage, Voluntariness and Future Usage, External Variables and Behavioral Intention
are not that significant in which effect of Perceived Behavioral Control on Behavioral
intention only pass the t-test.
From this result, we noticed that web-users with greater Behavioral Intention to
use the shopping website, they will tend to buy things online. Moreover, External
Variables also the main concern of web-users to choose whether to shop online. Trust
and privacy protection of online shopping websites may make web-users more
confident in purchasing from these shopping websites. Their Attitude toward using
the internet website may also affect their intention to shop online. However, the study
finds that social influence and facilitating conditions may not affect the web-users to
shop online. Without self experience, web-users may not choose to purchase online
due to their school mates and colleague. Also, they think that online shopping website
may not convenient than actual shop in which different kinds of products displayed
and got immediately without worrying any loss from delivery.
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Finally, after realizing the major concern of the customers, there are some
suggestions for e-business managers to build up a more beneficial maketing strategy
to maximize the profit.
3. Literature Review--------------------------------------------------------------- 10
7. Limitations and Future Research ------------------------------------------- 43
8. Conclusion ----------------------------------------------------------------------- 43
Appendix F: Summary result of hypotheses
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Technology development lead a dramatically increase in online shopping market
and the market is likely to keep its advantage in the future. However, there are still
some factors that interrupt e-business to totally capture the customers. It is crucial for
e-business to understand the concern behind the customers to shop online so as to
maximize their profit. The effects of these factors affecting web-users online shopping
are different among consumers with different insights toward online shopping.
Our research and finding not only can enhance the knowledge on behavior of
internet consumers, it can also provide web marketing for electronic business
(e-business) in order to establish the best direction of marketing strategy of e-business
and understanding on how to meet the needs of their online customers.
To help managers built up a framework for enhancing their e-commerce, our study
will focus on what and how can change customers’ attitude towards using the internet,
such as enhancing their shopping website with technologies that allow customers easy
to use and access their products, as well as gaining trust of existing customers.
A great previous model of examining technology acceptance of customers -
Technology Acceptance Model (TAM) has been tested repetitively by different areas
of studies and has been strongly supported. Amoroso & Hunsinger (2009) suggested
TAM “consistently explains a substantial proportion of variance in usage intentions
and behavior, among a variety of technologies”. The model in this study extends the
original TAM, consider other factors such as Voluntariness, Facilitating Condition,
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Social Influence and Perceived Behavioral Control so as to create a better predictor of
web-user to shop online.
2. Objectives of Study
The main objective of this study is to examine how each factor constructed in the
model affects web users to shop online in Hong Kong by extending the Technology
Acceptance Model (TAM) model.
2.1. Sub-objective:
2.1.1. To identify the key determinant and how they affect web-user to shop online
2.1.2 To provide web marketing for electronic business (e-business) in order to
establish the best direction of marketing strategy of e-business
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Figure 1 Amoroso and Hunsinger (2009) Research Model
Based on the study of Amoroso & Hunsinger (2009), a revised TAM model is
proposed to study what factors affecting consumers’ acceptance to online purchasing.
In Figure 1, it shows that different factors including Perceived Ease of Use, Perceived
Usefulness, Experience Using the Internet, External Variables (Trust, Risk and Privacy,
E-Loyalty, Perceived Value), Attitude toward using, Perceived Behavioral Control,
Behavioral Intention, Social Influence, Voluntariness, Facilitating Conditions may
have different extent to affect consumers’ acceptance to online purchasing.
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We used some construct of the model without concerning three factors: Perceived
Ease of Use, Perceived Usefulness, Experience Using the Internet.
Because using internet to find information is very convenient and common in Hong
Kong. Web-users have common perception on ease of use and usefulness of internet.
Most respondents may answer indifferently in this field.
Also, people of different age and education are significantly different in experience in
using the internet. The aged may use less or never use the internet, but youngsters
may spend much time on internet. The result may vary a lot if concerning this factor.
Thus, in this study, we focus on factors including External Variables (Trust, Risk and
Privacy, E-Loyalty, Perceived Value), Attitude toward using, Perceived Behavioral
Control, Behavioral Intention, Social Influence, Voluntariness, Facilitating Conditions
and to what extent these factors affect web-users to shop online.
This section also reviews previous articles, ranging from 1996 to 2009, which contain
constructs that we will use in our model.
Amoroso & Hunsinger (2009) suggested a modified Technology Acceptance Model
(TAM) to make the model more useful in explaining the acceptance of consumers to
internet-based technology. The relationship between attitude and behavior toward
using the Internet was supported. It was also supported that Future Usage is affected
by facilitating conditions and behavioral intention towards using the Internet. But,
relationship between voluntariness of using the internet and behavioral intention was
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not supported.
Shim et al. (2001) examined online information searching and product information to
be a key element in examining consumers purchase intention. In figure 1, the writer
foresaw that intention to use the internet for product information searching increases
the intention to use the internet for purchasing. Another hypothesis is also strongly
supported by the findings of the study that attitude toward internet shopping direct
affect the intention to use the internet for searching product information. In addition,
they hypothesized that a user's perception of "the extent to which significant referents
approve of internet use for shopping" has a direct relationship with "the user's
intention to use the internet for product information searching" (Shim et al. 2001).
There was also a strong correlation between perceived behavioral control and the
intention to use the internet for product information.
George (2002) used theory of planned behavior (TPB) to develop a model for
consumers’ online purchasing behavior. In figure 2, a strong correlation between how
trustworthy an individual finds on the internet and how positive an individual's
attitude is towards online purchasing and that an individual's attitude towards online
purchasing has strong effect on the intention to make them purchase by internet is
highly supported.
Van der Heijden et al. (2003) examined the factors affecting consumers’ intention to
online purchase. They found that the correlation between attitude towards using
internet for online purchases and behavioral intention is positive. In addition, they
found an obviously negative correlation between perceived risk and attitude towards
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using the internet for purchasing online. In figure 3, it shows the hypotheses that trust
affecting attitude towards using the internet for online purchasing was proved.
Bhattacherjee & Prekumar (2004) extended the expectation-disconfirmation theory to
include how users' belief and attitudes change during the exposure to IT usage. In
figure 4, it shows that both attitude affecting intention and satisfaction affecting
attitude was proved.
Kleijnen et al. (2004) tested the factors affecting adoption of mobile services for
e-commerce. In figure 5, it identified that system quality and social influence were
significant to the model. Attitude towards using mobile services has positive effect on
the intention to use mobile services. Research finding supported the positive
correlation between social influence and intention to use mobile services.
Elgarah & Falaleeva (2005) examined the effect of an individual's concern for privacy
on the adoption of biometric technology. It was supported that information privacy
has negative effect on intention to use the system which is voluntary.
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Figure2 Research model
Figure 2 shows the factors we used in the model. These factors can influence
web-user to future usage of online shopping website. We will hypothesize each
relationship from the right to left.
Voluntariness
Amoroso & Hunsinger (2009) predicted that voluntariness of using the Internet
have direct effect on future usage of the Internet. Their study shows support, but due
to the extremely weak correlation, the level of Voluntariness may not have strong
effect the Future Usage.
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H1: Individuals with a greater appearance of Voluntariness in using the Internet
will demonstrate a greater Future Usage of online shopping website.
Facilitating conditions
Venkatesh,et al. (2003) examined the facilitating conditions effect on behavioral
intention to use the technology and future usage. The study shows that there was
significant effect on Future Usage.
H2: The greater the Facilitating Conditions, the greater the on Future Usage of
online shopping website.
Perceived Behavioral Control
Ajzen (1991) refined Perceived Behavioral Control as individual perceptions of
how easy or difficult it is to perform a specific behavior and it is supported that
Perceived Behavioral Control have an effect on key dependent variables such as
intention and behavior.
Chau & Hu (2001) studied the application of information technology by targeting
the individual professionals. The writers predicted that perceived behavioral control
have effect on behavioral intention to use the business system. The hypothesis was
strongly supported.
Shim, et al. (2001) predicted perceived behavioral control have effect on
behavioral intention of users to use the system. The hypothesis was strongly support.
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H5: Individuals with a greater Perceived Behavioral Control will demonstrate a
higher Behavioral Intention to use the online shopping website.
H3: Individuals with a greater Perceived Behavioral Control will demonstrate a
greater Future Usage of the online shopping website.
Behavioral Intention
Elgarah & Falaleeva (2005) examined the adoption of biometric technology. The
authors predicted that information privacy have no effect on behavioral intention
when system use is mandatory, but have a negative effect while the system use is
voluntary.
consumers’ online purchasing, and their study found that behavioral intention will
have positive effect on future usage.
H4: The greater the Behavioral Intention results, the higher the Future Usage of
the online shopping website.
Social Influence
Chau and Hu (2003) studied the technology acceptance by school teachers, and
predicted that there is a relationship between social influence and behavioral intention
to use the system. The study shows significant relationship between these variables.
Venkatesh,et al. (2003) examined the adoption of personal computers using
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revised TAM and the result supported what the authors believed that social factors
have effect on behavioral intention when computer usage is mandatory.
Kleijen, et al. (2004) studied consumer acceptance of wireless finance. The
authors predicted that social influence directly affect the users’ behavioral intention to
use the system. The hypothesis was strongly supported.
H6: The greater the Social Influence, the greater the Behavioral Intention to use
the online shopping website.
Trust
“Consumers will be more willing to interact with and purchase products from a vendor
if they can trust that the vendor’s word can be relied upon that their vulnerabilities will not be
exploited”. (Geyskens et al. 1996)
It is reasonable to suggest that trust could also play a significant role in whether or not
consumers would be willing to surrender their biometric information in order to access their
bank accounts. For example, trust has been shown to increases one’s intention to share
personal information with an e-vendor (McKnight et al. 2002).
Risk and Privacy
Chen et al. (2004) studied consumers’ acceptance of virtual stores using
theoretical model and predicted that consumers’ perceived trust in a virtual store have
a positive effect on attitude toward using the e-store.
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Despite the tremendous growth in e-commerce over the recent past, it would be that
much greater if privacy and/or security issues were addressed more adequately in the eyes of
the consumer (Park & Kim 2003).
Miyazaki & Fernandez (2001) examined the customers’ privacy and risk
perceptions for Online Shopping. The writers hypothesized that a more perceived risk
of conducting online purchases a consumer with, it would be less frequency of
purchasing products online.
Holland & Baker (2001) examined customer participation on e-business for web
site brand loyalty, and they hypothesized that brand site loyalty create predictive
behavioral outcomes from customers.
Perceived Value
Kim & Xu (2004) studied the perceived price and trust in internet shopping and
they suggested that there is a positive relationship between perceived value
(trustworthiness) and purchase intention.
demonstrate a higher Behavioral Intention to the online shopping website.
H9: Individuals with positive perceptions towards external variables will
demonstrate a more positive Attitude towards using the online shopping website.
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Attitude toward Using
Van der Heijden et al. (2006) studied the acceptance of making purchases online.
The model examined the relationships between attitude towards using the Internet for
online purchases and behavioral intention. The correlation between attitude toward
using and behavioral intention was supported.
George (2001) studied the intention of users to make online purchases. The
author predicted that the more trust a user treats the Internet, the more positive the
user’s attitude toward using. The hypothesis was supported by a strong relationship
between an individual’s attitude toward using and the user’s behavioral intention in
the study.
H8: Individuals with a positive Attitude toward Using will demonstrate a higher
Behavioral Intention to use the online shopping website.
Future Usage
Future Usage refers to the amount of time a user spends on using the technology
(Amoroso & Hunsinger 2009). Amoroso and Hunsinger (2009) studied how Future
Usage is affected by behavioral intention of the technology. Their model shows that
behavior towards using the Internet has positive correlation with Future Usage of the
Internet.
Venkatesh et al. (2003) examined that behavioral intention towards personal
computers would have a positive effect on Future Usage of personal computers by
UTAUT model. This hypothesis was supported.
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4.2.1 Subjects
This study focuses on the factors affecting web-user to shop online and how the
combination of these factors moderates the intention to online purchasing.
The target respondents are the individual who has ever visited the online shopping
website and purchased something through the website. Therefore the respondents
should have some knowledge in online shopping website. The target interviewees
include both students and the workers.
Internet-based questionnaire was distributed to the Internet users in Hong Kong.
Two versions of questionnaires, English and Chinese (refer to Appendix A), were
prepared. A total of 383 responded to this research. 327 had experience in online
shopping, while 56 questionnaires are unusable because they are not target in this
research. Analysis in this study is based on those 327 respondents.
The respondents were asked to answer the questionnaires based on their online
shopping experience in an online store they used. Some examples of the online
shopping websites stated by the respondents were yahoo.com, amazon.com, ebay.com
and cityline.com.
Table 1 (refer to Appendix B) summarizes the demographic characteristics of the
respondents. As shown in the table, 40.3% of the respondents were males while 59.7%
of that was female. Over 68.9% of the respondents were 17-25 years old, 23.7% of the
respondents were 26-35 years old. The statistic shows that 47.7% of the respondents’
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education level attained university or above. Around 35.7% of the respondents were
student and 34% of the respondents had average monthly income below $4,000. All
respondents had purchased through the online shopping website. 50.26% of
respondents reported that they had purchased 1-3 times during the past six months
while 32.46% reported they had no purchase experience during the past six months.
4.2.2 Instrument
Subjects completed a questionnaire, including 34 items, shown in Appendix A.
At the beginning of the questionnaire, the respondents have been asked to indicate
whether they have visited and purchased from online shopping website. The purpose
was to screen out those who have no online shopping experience. We used previous
TAM model and research to derive various constructs to study their impact on the use
of internet technologies to purchase products. The content of the questionnaire was
divided into seven parts. They were the attitude towards online shopping, social
influence, perceived behavioral control, facilitating conditions, behavior intent to use,
voluntariness and trust, risk and privacy and, the last part, future usage.
Generally speaking, the scales of this questionnaire were adapted and modified
from prior studies with appropriate adjustment of wording to fit the specific needs of
this research. Five-point Likert scale is used for the questions in this part, ranging from
strongly disagree (1) to strongly agree (5). The last part of the questionnaire was the
demographic information including the gender, age, education level, occupation and
average monthly income.
We administered the survey through an online survey website, my3Q.
4.2.3 Procedure
During 8th March, 2010 to 8th April, 2010, an online survey was set up in the
online survey website my3Q. The survey was distributed to the students and workers
in different age and level of status and education. Respondents filled out the 34-item
questionnaires and then submitted to my3Q.
4.2.4 Analysis method
This study employed SmartPLS and SPSS for examining various factors and
their effect on web-user to shop online.
First, reliability will be calculated for the Attitude Toward Using, Social
Influence, Perceived Behavioral Control, Facilitating Conditions, Behavior Intention,
Voluntariness and External Variables, Future Usage to test the internal consistence.
Then we analyze what extent the measures relate to the factors and how the
independent variables affect the dependent variable.
4.3 Measurements
By reviewing other’s studies, scales were developed for measuring each of the
variables. Table 2 (refer to Appendix C) summarizes all the items and the sources of
those items. The following discussion describes the items used to measure each of the
variables.
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shopping was measured using four items, which were adapted from Amoroso and
Hunsinger (2009)
Social influence. Social influence was measured using four items, which two items
were adapted from Chau and Hu (2003) and two items were self constructed.
Perceived behavioral control. Perceived behavioral control was measured using four
items, which three items were adapted from Shim, et al. (2001) and one item was
adapted from Venkatesh,et al. (2003).
Facilitating conditions. Facilitating conditions was measured using three items,
which were adapted from Venkatesh,et al. (2003).
Behavior intent to use. Behavior intent to use was measured using three items, which
were adapted from Amoroso and Hunsinger (2009)
Voluntariness. Voluntariness was measured using three items, which one item was
adapted from Amoroso and Hunsinger (2009) and two items were self constructed.
External variables. External variables was measured using four items, which one
item was adapted from Miyazaki & Fernandez (2001), two items were adapted from
Chen et al. (2004) and one item was adapted from Holland & Baker (2001).
Future usage. Future usage was measured using six items, which three items were
adapted from Amoroso and Hunsinger (2009) and the other items self constructed.
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5.1. Participants
The target respondents are the individual who has ever visited the online shopping
website and purchased something through the website. Therefore the respondents
should have some knowledge in online shopping website. The target interviewees
include both students and the workers.
Internet-based questionnaire was distributed to the Internet users in Hong Kong.
Two versions of questionnaires, English and Chinese (refer to Appendix A), were
prepared. During the past one month data collection period, 383 questionnaires were
collected while 56 of them were not the target in this research. A total of 327 usable
questionnaires will be analyzed in this research.
The respondents were asked to answer the questionnaires based on their online
shopping experience in an online store they used. Some examples of the online
shopping websites stated by the respondents were yahoo.com, amazon.com, ebay.com
and cityline.com.
From the Table 1, the sample was made up of 130 (39.8%) of males and 197
(60.2%) females. Over 218 (66.6%) of the respondents were 17-25 years old, 83
(25.4%) of the respondents were 26-35 years old. The statistic shows that 157 (48.0%)
of the respondents’ education level attained university or above. Around 109 (33.3%)
of the respondents were student and 105 (32.1%) of the respondents had average
monthly income below $4,000.
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All respondents had purchased through the online shopping website. 192 (58.7%)
of respondents reported that they had purchased 1-3 times during the past six months
while 29 (21.1%) reported they had no purchase experience during the past six
months.
Measure Value Frequency Percent
Gender Male 130 39.8
17-25 218 66.6
26-35 83 25.4
36-45 13 4.0
Education level Primary school 0 0
Secondary school 76 23.3
Occupation Student 109 33.3
Clerical work 80 24.5
Managerial level 16 4.9
$4,000 - $8,999 64 19.6
$9,000 - $13,999 101 30.9
$14,000 - $18,999 30 9.2
Frequency (Past
six month)
Online website
5.2 Reliability of the Instrument
Cronbach’s Alpha was used to test the internal reliability of the scales.
Cronbach’s Alpha is an internal-consistency reliability estimation method. It should
only be computed on a homogeneous set of items (or questions). If the Cronbach’s
Alpha for the model constructs are at or above the recommended threshold of 0.7, the
construct are said to be reliable.
Table 2 summarizes the Cronbach’s Alphas for all scales. With alphas greater
than 0.6, the tests demonstrated that the measures of Attitude towards online shopping
(Att), Social influence (Soc), Perceived behavioral control (Per), Facilitating
conditions (Fac), Behavior intent to use (Beh), Voluntariness (Vol), External variables
(Tru) and Future usage (Fut) are reasonably internally consistent. For a more detail
reliability analysis result, please refer to Appendix D.
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Table 2 Cronbach’s Alpha Reliability Analysis (Substantial Level: Alpha >= 0.7)
Variables Items Original
Perceived behavioral
Facilitating conditions
(Removed
Fac3)
Voluntariness
(Removed
Future usage Fut1, Fut2, Fut3, Fut4,
Fut5, Fut6
0.708 0.708
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Table 3 summarizes the Mean and Standard Deviation of the variables
Variable Mean Standard Deviation
Social influence 2.752 0.943
Facilitating conditions 3.593 0.767
Behavioral intention 3.179 0.939
External variables 3.395 0.779
Future usage 3.322 0.851
From the Table 3, the mean of voluntariness is the highest 3.974. It shows that the
interviewee tend to agree on the voluntariness statements. Also, for the other variables,
the mean is larger than 3, it shows that the interviewee tend to agree the statements.
The mean of social influence is the lowest 2.752 and smaller than 3. It shows that the
interviewee tend to normal and disagree on the social influence statements.
5.3 Multiple Regression Analysis
Multiple regression is used to measure the relationship between several
independent or predictor variables and the dependent or criterion variable. To test the
hypothesis, when the p-value of regression coefficient is less than 0.05 (significant
level), then the independent variables affect the dependent variables. Otherwise, there
is no relationship between variables.
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Figure 3 Result for the research model
Figure 3 showed the overall explanatory power, estimated path coefficients, and
beta of the paths of the research model. Seven paths are found highly significant in the
research model while two of the paths facilitating conditions and social influence are
found insignificant.
Explaining Attitude toward using
In the first regression, an external variable is the independent variable and
attitude toward using is the dependent variable.
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The following multiple regression will be used to test Hypothesis 9. Attitude toward
using (Att) = 1.538 + 0.424*Tru.
(Att) = a + β1*Tru.
The results, presented in Table 4, show support for Hypothesis 9 as intensity of
external variables emerged as significant predictors of attitude toward using. Further,
the significant change in R-square (R²=0.185) indicated that 18.5% of the variance in
attitude toward using was explained by the external variables. Table 4 Regression
Result of Attitude toward using.
Table 4 Regression Result of Attitude toward using
Explaining Behavioral intention
In the second regression, external variables, attitude toward using, perceived
behavioral control and social influence are the independent variables and behavioral
intention is the dependent variable.
The following multiple regression will be used to test Hypothesis 5, 6, 7 and 8.
Behavioral intention (Beh) = 0.448 + 0.101*Per + 0.019* Soc + 0.207*Tru +
0.408*Att.
(Beh) = a + β1*Per + β2* Soc + β3*Tru + β4*Att.
The results, presented in Table 5, show support for Hypotheses 5, 7 and 8 as external
variables, attitude toward using and perceived behavioral control emerged as
significant predictors of behavioral intention. However, social influence are
insignificant since p-value of regression coefficient is larger than 0.05. Hence,
hypothesis 6 is rejected. Further, the significant change in R-square (R²=0.363)
indicated that 36.3% of the variance in behavioral intention was explained by the
external variables, attitude toward using and perceived behavioral control. Table 5
Regression Result of behavioral intention.
Table 5 Regression Result of Behavioral intention
Explaining Future usage
usage is the dependent variable.
The following multiple regression will be used to test Hypothesis 1, 2, 3 and 4. Future
usage (Fut) = 1.070 + 0.235*Vol + 0.079* Fac + 0.238*Per + 0.413*Beh.
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(Fut) = a + β1*Vol + β2* Fac + β3*Per + β4*Beh
The results, presented in Table 6, show support for Hypotheses 1, 2, and 4 as
perceived behavioral control, behavioral intention, voluntariness and facilitating
conditions emerged as significant predictors of future usage. However, facilitating
condition is insignificant since p-value of regression coefficient is larger than 0.05.
Hence, hypothesis 2 is rejected. Further, the significant change in R-square (R²=0.578)
indicated that 57.8% of the variance in future usage was explained by the perceived
behavioral control, behavioral intention, voluntariness. Table 5 Regression Result of
future usage.
Summary of results
After testing the hypothesis, a summary presentation of results is shown as
Figure 2. From the model, it indicates that behavioral intention (β=0.413) was a
strong determinant of continuance future usage, followed by perceived behavioral
control (β=0.238), then voluntariness (β=0.235). Moreover, attitude toward using
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(β=0.408) is the most significant determinant for behavioral intention, followed by
external variables (β=0.207), then perceived behavioral control (β=0.101). Finally,
external variables (β=0.424) is the most significant determinant for attitude toward
using. The test results for all hypotheses are shown in Appendix F.
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6. Discussions and Implications
The principle objective of this research is to examine factors that affect web users
to shop online in Hong Kong. It investigates the effect of voluntariness, facilitating
conditions, perceived behavioral control and behavioral intention on future usage.
Moreover, there is an effect of perceived behavioral control, social influence, external
variables and attitude toward using on behavioral intention. Finally, explores the effect
of external variables on attitude toward using.
Comparison with Amoroso and Hunsinger model
Table 7 shows the regression analysis and significant in this research and
Amoroso and Hunsinger (2009) research. Most of the hypothesis shows the same
significant level while hypothesis 2 and 6 are opposite from the Amoroso and
Hunsinger research.
*2 Facilitating
3 Perceived
36
control
5 Perceived
0.424 0.000 0.269 0.000
Note * the result got the different results from the Amoroso and Hunsinger research
Effect of Voluntariness on future usage
According to the finding, voluntariness was significant to future usage. The path
coefficient is 0.235. This result is consistent with previous studies (Amoroso &
Hunsinger, 2009). It shows the web users voluntary to shop online rather than
influence by others. Business managers can try to change the mind of the web user
that online shopping is required in life and let it be the part of their life. Therefore the
e-business companies should investigate more on the website and promote themselves
eventually. So that, the web user can pays more attention on the website. Additionally,
if the shopping website provides some unique products, the web user tend to
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According to the finding, facilitating conditions was insignificant to future usage.
The path coefficient is 0.079. This result is different from previous studies (Venkatesh,
2003) and Amoroso & Hunsinger research. It shows that facilitating conditions are not
important in Hong Kong. The research of Amoroso & Hunsinger is sampling the
undergraduate students in the United States and Australia. The geographic of United
States and Australia are different from Hong Kong. When you are living in United
States or Australia, you need to drive at list an hour to the shopping mall. It is trouble
for them to shop. But in Hong Kong, shopping mall is nearby. Everyone can shop
easily. Therefore, the e-business companies should maintain and even improve the
facilitating conditions of the website in order to improve the future usage, especially
the web user can find specialized product in the online shopping website as most of
the respondents have the complaints on this aspect. Moreover, the companies should
improve a good shorting engine for the web user and provide more kinds of products.
In the globalize world, if the online shopping websites can provide other countries
localize products for online shoppers. The market of online shopping can easily be
increased. For example, the Hong Kong web users can buy the Africa handmade
leather product.
Effect of perceived behavioral control on future usage
According to the finding, perceived behavioral control was the most significant
to future usage. The path coefficient is 0.238.This result is consistent with previous
studies (Shim, Eastlick, Lotz & Warrington, 2001) and Amoroso & Hunsinger
38
research. The perceived behavioral control (p<.001) are really significant constructs.
It seems that the model is quite robust in predict the future usage. It shows that if the
web user can control over how to shop online and have the necessary resources,
opportunities and knowledge would positively affect the future usage. The E-business
companies should maintain and even improve the website in order to improve the
future usage, especially the kind of resources the web user needed to shop online. The
mean of the resources needed is 3.716. It implies that if the web user fully got the
resources needed, they will tend to shop online. Therefore, the companies should
improve a convenient payment method for the web user and enhance more alternative
payment method. For example, in Hong Kong, the company can provide credit card
payment, bank account number for money transfer or cash mailing when confirm the
order of the product. So that, perceived behavioral control of the web user will tend to
more future usage the online shopping website.
Effect of behavioral intention on future usage
According to the finding, behavioral intention was the most significant to future
usage. The path coefficient is 0.413.This result is consistent with previous study
(Amoroso & Hunsinger, 2009). The perceived behavioral control (p<.001) are really
significant constructs. It seems that the model is quite robust in predict the future usage.
It shows that if the web users always try to use online shopping to find product would
positively affect the future usage. It is obviously that the web users always view the
online shopping website as a behavior. They will tend to shop if the product is useful
and suitable. The E-business managers should maintain and even improve the website
in order to improve the future usage. The mean of the web user always try to use online
39
shopping website to find product is 3.511. It implies that if the web users always view
the online shopping website, they are more willing to shop online. Therefore, the
companies should build up a trendy brand or set a cheapest price. Firstly, building up a
trendy brand can let the web users keep frequently update their knowledge. For
example, introducing the free fashion design from Japan and selling clothing together.
It attracts the web users’ intention to browse the website to make them shopping in the
future. Secondly, Managers can keep the lowest price of the limited product, making it
first-buy-first-served so that web users can keep on viewing the website.
Effect of perceived behavioral control on behavioral intention
According to the finding, perceived behavioral control was significant to
behavioral intention. This result is consistent with previous study (Ajzen, 1991) and
Amoroso & Hunsinger research. The path coefficient is 0.101. It shows the perceived
behavioral control would positively impact to behavioral intention. But this is a weak
significant because P=.049, just smaller than 0.05. The E-business can pay less
attention on this field. It’s not cost effective.
Effect of social influence on behavioral intention
According to the finding, social influence was insignificant to behavioral intention.
The path coefficient is 0.019.This result is different from the previous studies (Hu 2003,
Venkatesh et al. 2003 & Kleijnen et al. 2004) and Amoroso & Hunsinger research. The
mean of the web users use the shop online because of the proportion of colleagues and
friends who also use the internet to shop online is 2.541. It shows the web user tend to
disagree the statement. Most of them are not affected by others. This finding may due
to the existence of the weak influence or even potential influence. It is because online
40
shopping is not common and not totally accepted in Hong Kong. There are still some
web-users choose not to shop online. Therefore, it is difficult to be affected by the
multi-media, colleagues and friends, so the social influence is low too. It can not affect
the web users’ intention to shop online. Also, there is very less multi-media to promote
the shop online. The E-business companies should promote themselves more via the
social networking website, likes Facebook, Twitter, forum and so on. The effect on the
social networking website is quite large. The companies can open a Facebook group
for the users joined and provide relatively discount or rewards. At a result, it can
enhance the web user knowledge and discussion topic on the online shopping website.
Effect of external variables on behavioral intention
According to the finding, external variables was the most significant to behavioral
intention. The path coefficient is 0.207. This result is consistent with previous studies
(Chen et al. 2004, Geyskens et al. 1996) and Amoroso & Hunsinger research. The
external variables (p<.001) are really significant constructs. It seems that the model is
quite robust in predict the on behavioral intention. It shows that the web user concern
the trust, risk and privacy, e-loyalty, perceived value. The E-business companies
should maintain and even improve the website in order to improve the trust, risk and
privacy, e-loyalty, perceived value, especially the famous and well known of the online
shopping website. The mean of the resources needed is 3.771. It implies that if the web
user thought that the website is famous and well known, they will construct an
e-loyalty and perceived value to the website. Therefore, the companies should improve
a good image and construct a famous brand. For example, in Hong Kong, the Yahoo
auction shopping website is the well known website. Most of the web users tend to
shop in Yahoo auction rather than others. It is exactly our study that 209 (63.6%) web
41
users have a satisfied purchase experience in Yahoo auction. So, the external variables
were significant to behavioral intention.
Effect of attitude toward using on behavioral intention
According to the finding, attitude toward using was significant to behavioral
intention. The path coefficient is 0.408. This result is consistent with previous studies
George (2002) and Amoroso & Hunsinger research. It shows the attitude toward using
would positively impact to behavioral intention. The attitude toward using (p<.001) are
really significant constructs. It shows that web-users’ attitude toward using the online
shopping websites, they bring up an online shopping behavior. The E-business
companies should maintain and even improve the website in order is appealing. The
mean of online shopping is appealing is 3.529. It implies that if the online shopping is
appealing, the web used intent to online shopping. Therefore, the companies should
improve the design of website. Make the best use like light loading, visually appealing
design and graphics, effective content, and friendly user interface. All these facts will
surly impress anyone who suddenly just stops by the website, whether through the
search engine or word of mouth recommendation. Be prepared and be armed with a
high quality website. Therefore the effect of attitude toward using to the behavioral
intention is significant.
Effect of external variables on attitude toward using
According to the finding, external variables was significant to attitude toward
using. The path coefficient is 0.424. This result is consistent with previous studies
(Geyskens et al. 1996) and Amoroso & Hunsinger research. It shows the external
variables would positively impact to attitude toward using. The external variables
42
(p<.001) are really significant constructs. It shows that the web user concern the trust,
risk and privacy, e-loyalty, perceived value. Therefore, the external variables were
significant to attitude toward using.
Factor to affecting the web users to shop online must adapt to the technological
changes in the business world. More companies are selling over the internet than
before. Many physical stores are changing to online shop in Hong Kong due to the
extremely high rent for the physical stores. Our study and results helps the companies
to understand the web user attitude, behavior and usage.
When a start an e-business company, they need to focus on the perceived
behavioral control of the web user, provide a convenience payment method for the web
user and enhance more alternative payment choice. Also, the web user behavioral
intention, introduce the free trendily fashion design from other countries and selling
clothing together. It attract the web users intent to view the website, so that to affect
them shopping in the future. Furthermore, external variables of the website improve a
good image and construct a famous brand to build up the customer e-loyalty. Provide
sufficient security in the website to prevent any information loss. Last but not least, the
web user attitude toward using, the companies should maintain and even improve the
website in order it to be appealing.
43
7. Limitations and Future Research
Firstly, the sample size was not large enough to represent the whole population.
Due to the time limit, the survey was not evenly distributed to different age and the
majority of the respondent is student.
Also, the questionnaire designed in Chinese and English version. Different
respondents have different understanding when they did the survey in their language.
Therefore, questionnaire can be designed into one language either Chinese or English
version focus in the future research.
Future study may need to use a larger sample size to increase its reliability.
Moreover, extending the study by developing a more extensive model can increase the
power of the model such as the characteristics of the products that may affect the
customers’ intention to purchase online.
8. Conclusion
This research used a revised model of Technology Acceptance Model (TAM) and
previous research to study the factors affecting web-user to shop online. The
purpose of this study not only provide a insight of behavior of internet consumers, it
can also provide web marketing for electronic business (e-business) in order to
establish the best direction of marketing strategy and understanding on how to meet
the needs of their online customers so as to get the greatest benefit of their business.
The study concluded that a strong and positive relationship between Attitude
44
Toward Using the Internet and Behavioral Intention to use the internet so as to
increase the Future Usage of the internet, Perceived Behavioral Control and
Behavioral Intention to use the internet, Perceived Behavioral Control and Future
Usage of the internet.
It is also supported that individuals with positive perceptions towards External
Variables will demonstrate a higher Behavioral Intention to use the Internet. There
was strong relationship between External Variables and Attitude towards Using the
Internet, Voluntariness and Behavioral Intention, Facilitating Conditions and
Behavioral Intention.
Finally, Social Influence was not significant to predict the effect on Behavioral
Intention to use the internet and so to Future Usage.
45
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49
Appendices
Appendix F: Summary result of hypotheses
50

10.
11. 1 2 3 4 5

13. (,
14. 1 2 3 4 5
52


18.


24. 1 2 3 4 5


30. 1 2 3 4 5
31. 1 2 3 4 5
32. 1 2 3 4 5
33. 1 2 3 4 5
34. 1 2 3 4 5

35. :
Questionnaire on Factors affecting Web-user to shop online
I am a Year3 student studying Information Systems Management in HKBU. I am now
conducting a survey concerning your opinion toward online shopping. Please kindly spare a
few minutes to answer the following questions. The information you provide will be used for
academic purpose only. Thanks for your cooperation.
Part 1: Screening
1. Have you ever purchased anything through the online shopping website? (If you answer
“No”, this is the last question)
2. During the past six months, your frequency of online purchase is:
_____ none _____ 7-9 times
_____ 4-6 times
3. Can you name an online store which you have a satisfied purchase experience and answer
the following question based on this website? _______________________ (Continue with
Questions in Part II)
Part 2 Attitude towards online shopping 1-strongly disagree to 5-strongly agree
4. Online shopping is appealing 1 2 3 4 5
5. I like online shopping 1 2 3 4 5
6. It is a good idea to buy online rather than from a
physical store
1 2 3 4 5
7. I have fun when online shopping 1 2 3 4 5
Part 3 Social influence
8. I would be affected by multi-media to shop online 1 2 3 4 5
9. I would be affected by important person to shop online 1 2 3 4 5
10. I use the shop online because of the proportion of
colleagues and friends who also use the internet to shop
online
1 2 3 4 5
11. My colleagues and friends support me to shop online 1 2 3 4 5
Part 4 Perceived behavioral control
12. I have control over how to shop online 1 2 3 4 5
13. I have the resources needed to shop online (E.g. 1 2 3 4 5
55
Computer, credit card )
14. I have the necessary knowledge to shop online 1 2 3 4 5
15. In general, resources, opportunities, and knowledge
would be easy for me to shop online
1 2 3 4 5
Part 5 Facilitating conditions
16. I can find guidance in the online shopping website 1 2 3 4 5
17. I can find specialized product in the online shopping
website
1 2 3 4 5
18. I can find specific person or group to help when I have
difficulties in the online shopping website
1 2 3 4 5
Part 6 Behavior intent to use
19. I always try to use online shopping website to find
product
20. I use the online shopping website as many
cases/occasions as possible
1 2 3 4 5
21. I plan to use the online shopping website in the future 1 2 3 4 5
Part 7 Voluntariness
22. I am voluntary to shop online 1 2 3 4 5
23. I am not required to online shopping 1 2 3 4 5
24. Although the online shopping is convenience and fast,
it is not required to use
1 2 3 4 5
Part 8 Trust, Risk and Privacy
25. My online shopping website’s security measures are
sufficient
26. My private information is managed securely by my
online shopping website’s
27. My online shopping website is very famous and well
known
28. My online shopping website intends to fulfill its
promises
Part 9 Future usage
29. I will continue to shop online 1 2 3 4 5
56
30. I would consider shopping online in the short term 1 2 3 4 5
31. I would consider shopping online in the long term 1 2 3 4 5
32. I will continue to use the online shopping websites
that I have purchased from
1 2 3 4 5
33. I plan to purchase products or services from new
online shopping web sites
1 2 3 4 5
34. In general, I would buy online rather than going to a
physical store
Part 10 Personal Information
38. Occupation
_____ Student _____ Professional
_____ Below $4,000 _____ $14,000 – $18,999
_____ $4,000 – $8,999 _____ $19,000 – $23,999
57
Measure Value Frequency Percent
Gender Male 130 39.8
17-25 218 66.6
26-35 83 25.4
36-45 13 4.0
Education level Primary school 0 0
Secondary school 76 23.3
Occupation Student 109 33.3
Clerical work 80 24.5
Managerial level 16 4.9
$4,000 - $8,999 64 19.6
$9,000 - $13,999 101 30.9
$14,000 - $18,999 30 9.2
Frequency (Past
six month)
Online website
Attitude towards
online shopping
Amoroso and
Hunsinger, 2009
online rather than from a
physical store
Amoroso and
Hunsinger, 2009
shopping
multi-media to shop online
important person to shop
because of the proportion of
colleagues and friends who
online
support me to shop online
self constructed
shop online
Venkatesh,et al.,
to shop online (E.g. Computer,
credit card )
Per3 3. I have the necessary Shim, et al., 2001
61
opportunities, and knowledge
online
online shopping website
product in the online shopping
website
group to help when I have
difficulties in the online
shopping website to find
website as many
cases/occasions as possible
shopping website in the future
Amoroso and
Hunsinger, 2009
online
shopping
shopping is convenience and
self constructed
website’s security measures
Chen et al., 2004
managed securely by my
online shopping website’s.
well known.
Holland & Baker,
promises.
online
online in the short term
Amoroso and
Hunsinger, 2009
online in the long term
Amoroso and
Hunsinger, 2009
online shopping websites that
I have purchased from
or services from new online
shopping web sites
online rather than going to a
physical store
self constructed
Case Processing Summary
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.806 4
Item Statistics
rather than from a physical store
(Att3)
(Att4)
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.715 4
Item Statistics
multi-media to shop online
person to shop online (Soc2)
3.40 1.056 327
the proportion of colleagues and
friends who also use the internet
to shop online (Soc3)
2.96 .849 327
Case Processing Summary
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.833 4
Item Statistics
online (Per1)
shop online (E.g. Computer,
to shop online (Per3)
online (Per4)
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.562 3
.845 2
Item Statistics
shopping website (Fac1)
3.57 .751 327
the online shopping website
difficulties in the online
Case Processing Summary
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.792 3
Item Statistics
shopping website to find product
(Beh1)
as many cases/occasions as
website in the future (Beh3)
3.22 .931 327
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.584 3
Cronbach’s Alpha N of Items
1.000 1
Item Statistics
(Vol1)
shopping (Vol2)
convenience and fast, it is not
required to use (Vol3)
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.769 4
Item Statistics
security measures are sufficient.
shopping website’s. (Tru2)
(Tru3)
(Tru4)
a Listwise deletion based on all variables in the procedure.
Reliability Statistics
.708 6
Item Statistics
(Fut1)
3.28 .928 327
3.70 .806 327
shopping websites that I have
purchased from (Fut4)
3.77 .773 327
services from new online
shopping web sites (Fut5)
store (Fut6)
74
75
76
H1: Individuals with a greater appearance of Voluntariness in using
the Internet will demonstrate a greater Future Usage of online
shopping website.
H2: The greater the Facilitating Conditions, the greater the on
Future Usage of online shopping website.
Rejected
demonstrate a greater Future Usage of the online shopping website.
Accepted
H4: The greater the Behavioral Intention results, the higher the
Future Usage of the online shopping website.
Accepted
shopping website.
H6: The greater the Social Influence, the greater the Behavioral
Intention to use the online shopping website.
Rejected
Variables will demonstrate a higher Behavioral Intention to the
online shopping website.
shopping website.
variables will demonstrate a more positive Attitude towards using
the online shopping website.