Research Proposal in Partial Fulfilment of the Requirements of the Degree of International Master of Business Administration 2019 CHAPTER 1 4 INTRODUCTION

FONG JIA JUNN Research Proposal in Partial Fulfilment of the Requirements of the Degree of International Master of Business Administration 2019 CHAPTER 1 4 INTRODUCTION 4 1.1 Background of the Research Study 4 1.2 Research Problem 5 1.3 Research Aim 7 1.4 Research Objectives 8 1.5 Research Questions 9 1.6 Significance of the Study 10 CHAPTER 2 11 LITERATURE REVIEW 11 2.1 Dependent Variable (DV) – Consumers’ intention to use online financial services 11 2.2 Independent Variable 1 (IV1) – Security

An Empirical Research On The Factors
That Affecting The Consumers’ Intention
To Use Online Financial Services.
FONG JIA JUNN
Research Proposal in Partial Fulfilment of the
Requirements of the
Degree of International Master of Business Administration
2019
1

Table of Contents
CHAPTER 1........................................................................................................................................4
INTRODUCTION...............................................................................................................................4
1.1 Background of the Research Study..............................................................................................4
1.2 Research Problem........................................................................................................................5
1.3 Research Aim..............................................................................................................................7
1.4 Research Objectives.....................................................................................................................8
1.5 Research Questions......................................................................................................................9
1.6 Significance of the Study...........................................................................................................10
CHAPTER 2......................................................................................................................................11
LITERATURE REVIEW.................................................................................................................11
2.1 Dependent Variable (DV) – Consumers’ intention to use online financial services...................11
2.2 Independent Variable 1 (IV1) – Security...................................................................................12
2.3 Independent Variable 2 (IV2) – Advertising..............................................................................13
2.4 Independent Variable 3 (IV3) – Marketing Promotion..............................................................14
2.5 Independent Variable 4 (IV4) – Technology Acceptance Model (TAM)...................................15
2.6 Literature Review Summary......................................................................................................15
2.7 Research Model.........................................................................................................................16
CHAPTER 3......................................................................................................................................17
RESEARCH METHODOLOGY.....................................................................................................17
3.1 Research Methodology Overview..............................................................................................17
3.2 Research Design........................................................................................................................17
3.3 Data Collection Method.............................................................................................................18
3.4 Sampling Process.......................................................................................................................18
3.5 Data Analysis.............................................................................................................................19
3.6 Research Methodology Summary..............................................................................................20
CHAPTER 4......................................................................................................................................21
RESULTS...........................................................................................................................................21
4.1 Expected Outcome.....................................................................................................................21
4.2 Demographic Data Analysis................................................................................................23
4.3 Validity and Reliability Analysis.........................................................................................29
4.4 Research Question 1 Analysis..............................................................................................30
4.5 Research Question 2 Analysis..............................................................................................31
4.6 Research Question 3 Analysis..............................................................................................32
4.7 Research Question 4 Analysis..............................................................................................33
4.8 Research Question 5 Analysis..............................................................................................34
4.9 Results Summary.................................................................................................................36
2

Chapter 5............................................................................................................................................37
Discussion...........................................................................................................................................37
5.1 Conclusion...........................................................................................................................37
5.2 Research Findings Relationship with Previous Literature Reviews.....................................37
Chapter 6............................................................................................................................................39
Recommendation...............................................................................................................................39
6.1 Practical Implications..........................................................................................................39
6.2 Theoretical Implications......................................................................................................39
References..........................................................................................................................................41
Appendix.............................................................................................................................................54
Appendix 1 - Literature Review Summary......................................................................................54
Appendix 2 - Cover Letter for Survey Questionnaires.....................................................................58
Appendix 3 – Survey Overview Table.............................................................................................59
Appendix 4 - Survey Questionnaires...............................................................................................60
Table of Figure
Figure 1 DV Consequences Diagram....................................................................................................6
Figure 2 Impact of the research DV in solving the research problem diagram......................................7
Figure 3 Research Questions.................................................................................................................9
Figure 4 Significance of Study............................................................................................................10
Figure 5 Research Model.....................................................................................................................16
Figure 6 Sample Size Calculation........................................................................................................19
Figure 7 Expected Outcomes of Research Study.................................................................................23
Figure 8 pie chart showing the male and female proportion in the sample..........................................24
Figure 9 pie chart showing the distribution of the age group in the sample.........................................25
Figure 10 pie chart showing the distribution of the income level group in the sample........................26
Figure 11 pie chart showing the distribution of the educational level group in the sample..................27
Figure 12 pie chart showing the usage of online financial transaction in the sample...........................28
3

CHAPTER 1
INTRODUCTION
1.1 Background of the Research Study
As the continually growth of international economy, the institutional and market
completeness advanced, the commercial banking is forced to undergo rapid change.
Technology is the major key driver for these developments, which is breaching the regulatory
barriers in order to create new products, services and market opportunities. These products
and services includes delivering financial services in electronic and remote distribution
channel via mobile application or website at any time, everywhere with faster speed and
lower cost compared to the traditional retail banking systems (Faullant, 2008 ; Daniel, 1999 ;
Reiser, 1997). Thus, it helps to embrace the advance system-oriented business practices and
management process for all industries (Brodie et al, 2007; David et al, 2008).
Based on Hutchinson (2000), there are statistics indicate that Internet Banking shows
up more than 50% of all banking transaction with 15% expanding rate per year. Yet, data
information security and privacy being the primary concerns for both business and consumers
(Ernst & Young, 1999). Hence, the security and privacy of the Internet Banking transactions
as well as the processing of personal data information could be the issue for online financial
services adoption (Hutchinson & Warren, 2001).
The underlying models employed in this research given a better understanding of the
personal perception on security and privacy and attitudes factors that will potentially help to
consummate the intention to use the online financial services by giving supported data
information to increase the intention to use the services. In addition, this paper reports the
results of quantitative study of the on security, privacy and marketing strategy as well as their
attitudes towards online financial services. The findings will be useful for both banking
sectors and researchers who seek to further understand the factors relevant to online financial
services
4

1.2 Research Problem
Based on Gartner Group’s 1999 report, online banking has been rapidly growth from
10 million adults in 1999 to 35 million adults by year 2003 (Barto, 1999). Besides that,
China Merchants Bank has launch the first internet banking system in 1997 and spread
rapidly within mainland China (Li, 2002). Furthermore, Maybank was the first bank to
provide online financial services in Malaysia on June 15, 2000. However, this online
financial services have not been rapidly growth, implemented by the other banks and
substituted for traditional banking system. Moreover, there were about 60% of people
who used cheques or physical cash as primary source. According to Ndubisi & Shinti
(2006), Internet Banking in Malaysia is still in an infancy stage, which given difficulty for
banking industry to develop and use the online financial services and system.
There is research that shows evidence to support a positive relationship between personal
perceptions and the intention to use the online financial services. The personal
perceptions were found to be the consumers’ intention of using the online financial
services (Black et al, 2001). However, Ndubisi (2006) stated that there is an additional
dimension were found to influence people’s intention of use and the decisions making,
highlighting the personal attitudes of the intention and decision for using the online
financial services.
Hence, this research focus in the intention to use online financial services by users in
evidence among people in Malaysia. It will help in further understanding on the factors
that decreasing the intention to use the online financial services among people in
Malaysia.
The research problem aim to focus on the decreasing of the intention to use the online
financial services which lead the transaction volumes of all banking industries reduced.
Besides that, it will also rise in the Public Relation efforts. As a result, it will impact the
business performance and revenue of the bank. Hence, the bank will lose their brand
reputation, meanwhile increase the brand switching behavior.
5

The following shows the DV consequences diagram.
Figure 1 DV Consequences Diagram
6

1.3 Research Aim
This research aims to increase the intention to use the online financial services among
people in Malaysia in order to increase the transaction volumes and brand reputation,
meanwhile minimize the Public Relation efforts and brand switching behavior of all
banking industries. Hence, the business performance and revenue of the bank will
increase.
The following shows diagram of the impact of the research DV in solving the research
problem.
Figure 2 Impact of the research DV in solving the research problem diagram
7

1.4 Research Objectives
Based on the research aim, there are several research objectives been developed in this
research study develops as shows in the following:
RO1 – To examine the relationship between security and consumers’ intentions to use
online financial services.
RO2 – To analyze the relationship between advertising and consumers’ intentions to
use online financial services.
RO3 – To investigate the relationship between marketing promotions and consumers’
intentions to use online financial services.
RO4 – To assess the relationship between Technology Acceptance Model (TAM) and
consumers’ intentions to use online financial services.
RO5 – To evaluate the most important factor, such as security, advertising,
Technology Acceptance Model (TAM) and marketing promotions that impacting the
consumers’ intentions to use online financial services.
8

1.5 Research Questions
Based on the preceding research objectives, there are five research questions being discussed
as follow:
Figure 3 Research Questions
9

1.6 Significance of the Study
The figure below shows the research significance of this case study.
Figure 4 Significance of Study
Consequently, this research study seek to enhance researcher understanding of the
relationship specifically between consumers’ intention on security, advertising, Technology
Acceptance Model (TAM) and marketing promotions toward using online financial services.
Furthermore, the research will also examine the effect of the DV towards the research
problems as well as research significances. Hence, the impact, such as profitability will be
determined. In addition, all the independent variables will be examined and analyses on
which factor is the most impact to the DV.
10

CHAPTER 2
LITERATURE REVIEW
The researcher begins the chapter with the meaning of online financial services.
According to The Federal Trade Commission Consumer Information (2012), online financial
services known as electronic banking, which could be defined as Electronic Fund Transfer
(EFT). Furthermore, online financial services could also be defined as a mobile electronic
and remote distribution channel to conduct or deliver the financial activities on a virtual
environment (Bradley 2002; Reiser, 1997). Hence, paper transactions have been replaced by
computer and information technology in the online financial services’ infrastructure. In
addition, online financial services have included ATM, Fund Transfer, Direct Deposit, Pay-
by-Phone Systems, Debit or Credit card transaction, website and other applications. .
2.1 Dependent Variable (DV) – Consumers’ intention to use online
financial services
Consumers’ intention can be defined as the interest of individuals toward using the
online financial systems offered for future financial transactions (Ajzen, 1991). Based on
Davis (1986, p.28), consumers’ behavioral intention can be defined as the strength of the
expected consumers’ intention to pursue or support the decision of use in their mind.
Behavioral intention is related to the attitude of an individual’s either positive or negative
feelings about performing an action ((Fishbein & Ajzen, 1975). Hence, there are a number of
case studies of the individual intention towards the use of online financial services.
There were major research study conducted by Foxall & Goldsmith (1988); Manning
et al, (1995), which focus on identifying the congenial consumers’ intentions and new
product adoption behavior. Walton & Leavitt (1975) defined personal intention in terms of an
individual’s willingness to adopt and use new product experiences. There is also an example
of research study shows that an individual has a greater intention to use mobile banking are
more likely to have positive attitude towards using online transaction systems (Lee, 2003).
Furthermore, the intention to use can be formed through past transaction behavior, direct
experiences with the services, advertisement, word-of-mouth information and others
(Schiffman & Kanuk, 1997). According to Davis (1986, p.25) defined intention as the
personal degree of assess appertain toward the attitude behavior. Furthermore, a consumers’
11

intention toward using a product or service is a primary determinant of the attitude to
practical use (Davis, 1986; Mathieson, 1991; Venkatesh & Davis, 1994). Meanwhile,
research study conducted by Flavia´n and Guinalı´u (2006) has identified that the consumers’
perceptions on the security of the personal data information handling process is the major
factor that affect the intention to purchase online. Hence, the following section will be discuss
on the security factor as one of the Independent Variables (IV) in this research study.
2.2 Independent Variable 1 (IV1) – Security
Security can be defined as a menace which brought circumstance or activity with
latent to cause economic tribulation to data information or network resources in the form of
disclosures, devastation, and modification of data information, fraud or denial of services
(Kalakota & Whinston, 1997, p. 853). Meanwhile, there are a number of case studies that
investigated on the influences of security factor towards the consumers’ behavioral intention
and perception.
There are a number of case studies conducted by Jarvenpaa et al (1999), Gefen
(2000) and Wang et al (1998), stated that security is one of the primary barriers for the online
financial services to growth as the personal data information could be used for fraudulent
activity. On the other hand, Klang (2001) and Grabner-Kra¨uter & Kaluscha, (2003) have
stated in their research studies, which the consumers’ subjective security perception has given
bigger impact towards the acceptance of e-commerce adoption rather than the objective
security of the mobile transaction channel. Besides that, security issue tend to be the most
significant element that motivated Chinese consumers toward adoption of online financial
services (Laforet & Li, 2005). In addition, the security and privacy trait and function of the
online financial website along with shared valence are the primary element of trust which
could positively influences the consumers’ behavioral intentions toward online financial
services (Mukherjee & Nath, 2007).
In a nutshell, the security factor which influence the consumers’ intention and user
acceptance to use, has been supported by a number of authors in the context of online
financial services (Poon, 2007; Sathye, 1999; Liao & Cheung, 2002). Hence, security has a
12

positive effect towards the consumers’ intention to conduct online transaction (Ranganathan
& Ganapathy, 2002; Kim et al., 2008).
Therefore:
RQ1 – What is the impact of security on consumers’ intentions to use online
financial services?
2.3 Independent Variable 2 (IV2) – Advertising
The term “advertising” defined as a way which a business owner provides
information details to promote their activities, products, services, pricing and delivery
channel in order to attract people from existing or future consumers (Ennew et al, 1995). In
the financial industry, advertising is one of the effective tool which used by banks in order to
create awareness for their customers on the services provided. However, ads with valuable
information will attract more consumers (Passadeos, 1990). Hence, there are several research
studies that conducted to investigate the relationship between advertising and consumers’
intention.
There are research studies conducted by Chan (2004) and Adhi (2009) to investigate
the influences of advertising towards users’ intention and perceptions. The research outcome
concluded advertisement as one of the important factor for all financial industries to promote
and informing their existing or future customers about the products and services offered by
the banks. Furthermore, advertisement could help to create awareness and knowledge
information on the benefits of the consumers in the society (Rettie et al, 2003). Meanwhile,
consumers with more positive intentions and perceptions toward advertisements generally
tend to spend more time to explore at the ads as well as recall more advertisements in future
(James & Kover, 1992; Donthu et al, 1993). There is a survey conducted by DoubleCLick
(2004), justified that there are more than 60 percent of people who develop positive intention
towards the advertisement sender as well as build a positive influence towards the
informative advertisement.
Therefore:
13

RQ2 - What is the impact of advertising on consumers’ intentions to use online
financial services?
2.4 Independent Variable 3 (IV3) – Marketing Promotion
According to Chandrashekaran and Grewal (2003), price promotions are a common
promotional activity, which are normally aimed to enhance consumers’ perceptions of value
and increasing the likelihood of purchase (Grewal et al., 1998). There are two types of
marketing promotions, such as price-oriented and non-priced-oriented promotion (Kotler et
al, 1999).
Based on Bawa and Shoemaker (1989), price-oriented promotions could help to
achieve short-term goals, such as increase sales volumes and the company market shares.
Meanwhile, inducing product trial usage can also help to increase the users’ intention towards
the product or service offered by the company (Gupta, 1988; Blattberg & Neslin, 1990). For
non-price-oriented promotions adoption have helped the company to enhance their brand
reputation, customer loyalty as well as strengthen the brand associations, which lead to
develop positive consumers’ attitudes and intentions to use the products and services
(Conlon, 1980; Jagoda, 1984; Aaker, 1991). Non-price-oriented promotions are often
perceived as gaining customer attraction, view, loyalty and brand image whereas the price-
oriented promotions are often used for minimizing losses (Campbell & Diamond, 1989).
Hence, there is a research study in Singapore examined that majority of the departmental
retail stores are using rebates system and contest in order to increase the excitement of the
consumers toward the brand as well as offered valuable advertising resources and generating
consumers’ intention and awareness (Wah Lee, 2002).
Therefore:
RQ3 - What is the impact of marketing promotions on consumers’ intentions to use
online financial services?
14

2.5 Independent Variable 4 (IV4) – Technology Acceptance Model
(TAM)
The Technology Acceptance Model (TAM) was introduced by Davis in 1986 and
defined as a theory to investigate the user behavior intention towards the information
technology (IT). According to Davis (1986), the primary goal of TAM is to examine the
determinants of IT acceptance across extensive extent of information technologies as well as
user populations. Furthermore, acceptance of IT can be firmed by two key factors, such as
perceived usefulness and perceived ease of use which affect a users’ intention to use a
technology (Davis et al, 1989).
There are a number of research studies conducted by using Technology Acceptance
Model (TAM). Baumeister (2002) using TAM to carry out a research study to investigate the
number of times an individual performed online transaction throughout the past six months.
Furthermore, TAM has also been used by Fishbein and Ajzen (1957) in their research study
to examine a person’s perception on the way people deems towards his or her behavior and
the motivation to carry out with the expectations of these referents. Besides that, there are
also case studies subsequent to Davis (1989) argued that TAM yields are more likely to be
have a consistent outcome on the users’ acceptance behavior towards new technology
systems in the workplace environment (Adams et al, 1991; Chin & Gopal, 1995; Lu & Yeh,
1998; Mathieson, 1991). Furthermore, there are a number of research studies further modified
and extended the TAM to access the variables, such as Internet availability, speed and cost
would affect the users’ behavior and intentions to use the technology (Agarwal & Prasad,
1997; Teo, 2002; Guimararaes & Davis, 1995; Gefen & Straub, 1997).
Therefore:
RQ4 – What is the impact of Technology Acceptance Model (TAM) on consumers’
intentions to use online financial services?
2.6 Literature Review Summary
The literature review summary will be shows in Appendix 1.
15

2.7 Research Model
The following figure shows the research model of this case study.
Figure 5 Research Model
16

CHAPTER 3
RESEARCH METHODOLOGY
The researcher in this case study has covered the research methodology, which used
to carry out the research study effectively, includes a discussion on the process of selecting
the research design which will be suitable for this research case. Besides that, the population
and sampling for this research study will be decided using statistical techniques. Moreover,
there is also a section to present the data collection procedure. Hence, a further discussion on
determining the data analysis tool, follow by a short summary to conclude Chapter 3.
3.1 Research Methodology Overview
This research study was conducted in order to examine the consumers’ intention to
use online financial services in Malaysia. The focus of the assessment was on the factors,
such as security, advertising, marketing promotions and Technology Acceptance Model
(TAM). In order to gather necessary data in a shorter period with limited interaction, the
researcher utilized the quantitative approach. Survey questionnaires is conducted to collect
numerical data to test and justify the RO and RQ. A further discussion will be included in the
following sections.
3.2 Research Design
The research design forms an integral part of the research methodology as it tends to
reflect the overall manner in which the study shall be concluded and the manner in which the
readers will be able to derive relevant information (Kumar, 2019). Hence, it has been
decided that the research design which was adopted for the particular research can be
understood to be the deductive in nature with respect to which the study will take a
correlational format. In this type of a study, the relationship between the intentions to use the
online transactions and the factors influencing the choice were assessed (Quinlan et al.,
2019). This would enable the researcher to present the overall study in a comprehensive
manner in line with which, the research objectives can be achieved successfully. Moreover,
the answer to the different research questions can also be provided in an adequate manner.
17

3.3 Data Collection Method
The data collection method can be understood to be a useful part of the research
methodology as it identifies and defines the manner in which the data is collected for the
purpose of the research. For this study, quantitative research is being used, and the data
collection method which was adopted can be understood to be the primary method of data
collection (Peffers et al., 2007). Within the primary method of data collection, the survey
method of collecting the responses from the different respondents was used to identify
individuals’ attitude, perceptions, behavior, concerns or knowledge. By using this method, it
can be essentially mentioned that the cross sectional data collection time frame was chosen
for the research. Furthermore, the survey will assist the researcher in understanding the
overall audience and in a manner similar to this, the overall objectives of the research can be
successfully achieved. Hence, the survey questionnaires cover letter and the sample is
attached in the Appendix 2 and 3. According to Cooper et al (2009), behavioral intention is a
summary evaluation which in terms of predisposition, includes positive or negative intention
towards a new product. Hence, the positive or negative attitude will be reflected in behavioral
reactions. The seven-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly
agree) will be used in the survey questionnaires.
3.4 Sampling Process
As the primary method of the data collection has been chosen, it becomes essentially
crucial for the researcher to consider that they adopt a comprehensive approach towards the
overall research procedure. Therefore, it is for this reason that the research made use of the
simple random probability method (Marczyk, DeMatteo & Festinger, 2005). With respect to
this, the different respondents which were chosen for the purpose of the study can be
understood to be the individuals in Malaysia. According to Sekaran, Cavana & DeLahaye
(2001), the sample size must be sufficient and representative of that population, in order to
conduct a study that attempt to make generalized findings which specifically applicable to a
particular population. A-priori sample size calculator was used to calculate the sample size
(Soper, 2018).
According to Malaysia Population (2019), the estimated population in Selangor,
Malaysia is around 5.9 million. The following shows the sample size calculation outcome:
18

Figure 6 Sample Size Calculation
Hence, the sample size which was chosen for the study can be understood to be 600.
Although the sample size of 600 can be understood to be a considerably small sample size,
however, due to certain time and other restrictions the sample size of 600 can be considered
to be sufficient for representing the population. In regard to this, it can also be stated that the
systematic random sampling shall be essentially made use of which will assist in
understanding the overall factors which tend to influence the use of online transactions by the
different customers which are present in the particular industry.
3.5 Data Analysis
The Data analysis tends to form a critical part of the research study and with respect
to this, it can be considered relatively important for the researcher to undertake an extensive
data analysis procedure. The data analysis procedure which has been adopted for the purpose
of the study can be understood to be the quantitative approach. In line with this, the
researcher has collected the responses and then apply the different statistical tests relating to
19

the correlation and regression which shall help in understanding the impact of the factors on
the intention to use the online transactions (Mackey & Gass, 2015). The use of cross sectional
study has also been adopted. Moreover, the descriptive statistics was also used as a medium
to describe and examine the overall demographics which was made use of for the purpose of
the paper. Linear regression helps to determine the relationship between one single
independent variable and one single dependent variable while multiple regression is use to
analyze the relationship between more than one independent variables and a single dependent
variable.
3.6 Research Methodology Summary
Therefore, it can be rightfully mentioned that the third chapter, the Research
Methodology chapter can be considered to be an essential chapter which identifies the overall
research techniques and related methods which are generally made use of for the purpose of
the study. In line of this, it can be understood crucial for the researcher to be able to explain
the data analysis, data collection as well as the sampling technique which will serve the
research objectives (Lloret, 2016). The chapter defined that the paper had made use of the
primary data collection method, the Simple Probability Sampling technique and the
quantitative analysis method of analysis. The given procedures have been deemed to be
appropriate for the research study because it will help in presenting the overall findings in the
right manner and the different methods used also align well with the research aims and
questions. These will assist in answering the questions with complete evidence and approach.
20

CHAPTER 4
RESULTS
The researcher present and discuss the overall findings and descriptive analysis results
with reference to the aim of the study, which was to examine the consumers’ intention to use
online financial services in this chapter. Further discussions on the result findings will be
discussed in the following sections.
4.1 Expected Outcome
The outcome of the statistical analysis on the collected data can be expected from the
previous research works that are discussed in the literature review part.
EQ1 discusses about the expected result of the RQ1 which wants to explore the impact of
security on consumers’ intentions to use online financial services. The influence of the security on the
consumers’ intentions to use online financial services is discussed in literature review that says
security issues are more likely to be the most significant factor in motivating the Chinese
consumers to adopt the online financial services (Laforet & Li, 2005). Moreover, the security
and privacy trait and function of the online financial website are the primary element of trust
which are potential enough to influence behavioral intentions of consumer towards online
financial services (Mukherjee & Nath, 2007). Hence, it is expected that security will have a
positive influence on the consumers’ intentions to use online financial services.
EQ2 discusses about the expected result of the RQ2 which wants to explore the impact of
advertising on consumers’ intentions to use online financial services. The influence of the
advertising on the consumers’ intentions to use online financial services is discussed in literature
review that says advertisement is an important factor for all financial industries in order to
promote and inform their existing or future customers about the products and services offered
by the banks. There is a survey conducted by DoubleCLick (2004), justified that there are
more than 60 percent of people who develop positive intention towards the advertisement
sender and also build a positive influence towards the informative advertisement. Hence, it is
expected that advertisement will have a positive influence on the consumers’ intentions to use
online financial services.
21

EQ3 discusses about the expected result of the RQ3 which wants to explore the impact of
marketing promotions on consumers’ intentions to use online financial services. The influence
of marketing promotion on the consumers’ intentions to use online financial services is discussed in
literature review that says majority of the departmental retail stores are using rebates system
and contest in order to increase the excitement of the consumers toward the brand as well as
offered valuable advertising resources and generating consumers’ intention and awareness
examined by a research study in Singapore (Wah Lee, 2002). Hence, it is expected that
marketing promotions will have a positive influence on the consumers’ intentions to use online
financial services.
EQ4 discusses about the expected result of the RQ4 which wants to explore the impact of
Technology Acceptance Model (TAM) on consumers’ intentions to use online financial
services. The influence of Technology Acceptance Model on the consumers’ intentions to use
online financial services is discussed in literature review that says TAM yields are more likely to
be have a consistent outcome on the users’ acceptance behavior towards new technology
systems in the workplace environment (Adams et al, 1991; Chin & Gopal, 1995; Lu & Yeh,
1998; Mathieson, 1991). Hence, it is expected that marketing promotions will have a positive
influence on the consumers’ intentions to use online financial services.
EQ5 discusses about the expected result of the RQ5 which wants to explore the most
influential factor among security, advertising, marketing promotions and Technology
Acceptance Model (TAM) on consumers’ intentions to use online financial services. The
influence of all the independent variables on the consumers’ intentions to use online financial
services is discussed in literature review that says TAM have been extended in several research
studies to access the variables, such as Internet availability, speed and cost would affect the
users’ behaviour and intentions to use the technology (Agarwal & Prasad, 1997; Teo, 2002;
Guimararaes & Davis, 1995 and Gefen & Straub, 1997). TAM yields are more likely to be
have a consistent outcome than other factors on the users’ acceptance behaviour towards new
technology systems in the workplace environment (Adams et al, 1991; Chin & Gopal, 1995;
Lu & Yeh, 1998) and Mathieson, 1991) Hence, it is expected that TAM will have the most
influential factor in terms of having the greatest impact on the consumers’ intentions to use
online financial services.
The expected outcome of the analysis is presented in the following figure:
22

Figure 7 Expected Outcomes of Research Study
4.2 Demographic Data Analysis
This section discusses the demographics of the observations that incorporates the variables
gender, age, Income Level (Annual), Income Level and usage of Online Financial Transaction. All
these demographic details are discussed below one by one in following sections:
23

There are total 638 observations of which 63.5 percent respondents are female and 36.5 percent
respondents are male. This implies there are more females than males. The pie chart of gender
presented in figure 8, represents the result using the pie chart which shows that the area of female has
covered most of the total area.
Table 1: Distribution of respondents across gender.
Gender
Frequency Percent Valid Percent Cumulative Percent
Valid Male 233 36.5 36.5 36.5
Female 405 63.5 63.5 100.0
Total 638 100.0 100.0
Figure 8 pie chart showing the male and female proportion in the sample
24

Most of the respondents belong from the young age which indicates the respondents whose age
lies between 18 and 29 years. Almost 56% of the respondents belong to this age group. The age group
30-45 years, 46-59 years and above 60 years has been recorded 29.2%, 10.3% and 29% of the sample.
There are less amount of people from the age group that belongs to 40-59 years of age. The pie chart
of age group presented in figure 9 visualizes the same result where it is clearly seen that the most
people belongs to the age group 18-29 years and the least people belongs to the age group 40-59
years.
Table 2: Distribution of respondents across age group.
Age Group
Frequency Percent Valid Percent
Cumulative
Percent
Valid 18-29 Years Old 357 56.0 56.0 56.0
30 - 45 186 29.2 29.2 85.1
46 - 59 66 10.3 10.3 95.5
60 and above 29 4.5 4.5 100.0
Total 638 100.0 100.0
Figure 9 pie chart showing the distribution of the age group in the sample
25

Most of the respondents belong from the income level of RM30,001-RM50,000. Almost 39.3%
of the respondents belong to this income level group. The income level group RM30,000 and below,
RM50,001 - RM60,000 and RM60,001 and above has been recorded 33.1%, 21.6% and 6% of the
sample respectively. There are less amount of people from the income level group RM60,001 and
above. The pie chart of income level group presented in figure 10 visualizes the same result where it
is clearly seen that most of people belongs to the income level group RM30,001-RM50,000 and the
least people belongs to the income level group RM60,001 and above.
Table 3: Distribution of respondents across income level.
Income Level
Frequency Percent Valid Percent
Cumulative
Percent
Valid RM30,000 and below 211 33.1 33.1 33.1
RM30,001 - RM50,000 251 39.3 39.3 72.4
RM50,001 - RM60,000 138 21.6 21.6 94.0
RM60,001 and above 38 6.0 6.0 100.0
Total 638 100.0 100.0
Figure 10 pie chart showing the distribution of the income level group in the sample
26

Most of the respondents are University Graduate. Almost 38.4% of the respondents belong to
this education level group. The educational level group High School, College and University
Postgraduate has been recorded 29.6%, 27.6% and 4% of the sample respectively. There are less
amount of people from the education level group post graduate. The pie chart of education level group
presented in figure 11 visualizes the same result where it is clearly seen that the most people belongs
to the education level group university graduate and the least people belongs to the education level
group post graduate.
Table 4: Distribution of respondents across education level.
Education Level
Frequency Percent Valid Percent
Cumulative
Percent
Valid High School 189 29.6 29.6 29.6
College 176 27.6 27.6 57.2
University Graduate 245 38.4 38.4 95.6
Post Graduate 28 4.4 4.4 100.0
Total 638 100.0 100.0
Figure 11 pie chart showing the distribution of the educational level group in the sample
27

There are total 638 observations of which 90.9 percent respondents are the user of online
financial transaction and 9.1 percent does not do the online financial transaction. The pie chart of
usage of online financial transaction group presented in figure 12 visualizes the same result where it is
clearly seen that most of the people are the user of online financial transaction.
Table 5: Distribution of respondents across usage of online financial transaction.
Usage of Online Financial Transactions
Frequency Percent Valid Percent Cumulative Percent
Valid Yes 580 90.9 90.9 90.9
No 58 9.1 9.1 100.0
Total 638 100.0 100.0
Figure 12 pie chart showing the usage of online financial transaction in the sample
28

4.3 Validity and Reliability Analysis
The validity and reliability analysis is required to determine to know how much the variability in
the variables is present because of errors generated from the measurement process and how much
variability is present because of true values. The Cronbach’s alpha represents the internal consistency
which means how closely the variables are related to each other. This is considered as measure of
reliability. The alpha value presents the consistency or thee coefficient of reliability. In the table 6,
various tables are generated through Cronbach’s alpha reliability test to check the reliability of the
data set on which statistical tests will be performed. The tables of result is presented below:
Table 6: Cronbach’s alpha reliability test
Reliability Statistics
Cronbach's
Alpha
N of Items
.940 5
Item-Total Statistics
Scale Mean if
Item Deleted
Scale Variance
if Item Deleted
Corrected Item-
Total Correlation
Cronbach's
Alpha if Item
Deleted
Security 16.4665 7.837 .781 .936
Advertising 16.3633 7.629 .867 .920
Marketing Promotion 16.3369 7.725 .834 .926
Techonology Acceptance
Model (TAM) 16.3746 7.520 .855 .922
Consumer Behavioral
Intentions 16.4093 7.522 .848 .923
Scale Statistics
Mean Variance Std. Deviation N of Items
20.4876 11.770 3.43074 5
The table reliability statistics presents the Cronbach’s alpha which is 0.940. The acceptable
value of the Cronbach’s alpha is 0.7 and above. Here the value of the Cronbach’s alpha is too high
which simply indicates that the data set is reliable to perform the analysis.
29

4.4 Research Question 1 Analysis
What is the impact of security on consumers' intentions to use online financial services?
In order to find out the influence of security on consumers' intentions to use online
financial services, the regression analysis is performed and the result tables are presented
below:
Table 7: Regression analysis of security on consumers' intentions to use online financial
services
Model Summary
Mode
l R
R
Square
Adjusted R
Square
Std. Error
of the
Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .709a .503 .502 .55128 .503 644.249 1 636 .000
a. Predictors: (Constant), Security
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 195.794 1 195.794 644.249 .000b
Residual 193.287 636 .304
Total 389.082 637
a. Dependent Variable: Consumer Behavioral Intentions
b. Predictors: (Constant), Security
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) 1.167 .117 9.996 .000
Security .724 .029 .709 25.382 .000
a. Dependent Variable: Consumer Behavioral Intentions
The table summary presents the value of R-square which is 0.503. This implies that
the model can explain 50.3% of the variability of the dependent variable. The F-stat of the
ANOVA is 644.249 with p-value 0.00 which implies the model is better than the intercept
model which is significant at 5% significance level. Now, the intercept is 1.167 with p-value
30

0.00 and the coefficient of security is 0.724 with the p-value 0.00. Both the intercept and
slope terms are positive and statistically significant at 5% significance level. This implies that
the security has a positive impact on consumers' intentions to use online financial services.
4.5 Research Question 2 Analysis
What is the impact of advertising on consumers' intentions to use online financial services?
In order to find out the influence of advertising on consumers' intentions to use online
financial services, the regression analysis is performed and the result tables are presented
below:
Table 8: Regression analysis of advertising on consumers' intentions to use online financial
services
Model Summary
Model R
R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .775a .601 .601 .49384 .601 959.425 1 636 .000
a. Predictors: (Constant), Advertising
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 233.978 1 233.978 959.425 .000b
Residual 155.103 636 .244
Total 389.082 637
a. Dependent Variable: Consumer Behavioral Intentions
b. Predictors: (Constant), Advertising
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) .735 .110 6.702 .000
Advertising .811 .026 .775 30.975 .000
a. Dependent Variable: Consumer Behavioral Intentions
31

The table summary presents the value of R-square which is 0.601. This implies that
the model can explain 60.1% of the variability of the dependent variable. The F-stat of the
ANOVA is 959.425 with p-value 0.00 which implies the model is better than the intercept
model which is significant at 5% significance level. Now, the intercept is 0.735 with p-value
0.00 and the coefficient of advertising is 0.811 with the p-value 0.00. Both the intercept and
slope terms are positive and statistically significant at 5% significance level. This implies that
the advertising has a positive impact on consumers' intentions to use online financial services.
4.6 Research Question 3 Analysis
What is the impact of marketing promotions on consumers' intentions to use online financial
services?
In order to find out the influence of marketing promotions on consumers' intentions to
use online financial services, the regression analysis is performed and the result tables are
presented below:
Table 9: Regression analysis of marketing promotions on consumers' intentions to use online
financial services
Model Summary
Mode
l
R R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .759a .575 .575 .50965 .575 861.937 1 636 .000
a. Predictors: (Constant), Marketing Promotion
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 223.884 1 223.884 861.937 .000b
Residual 165.198 636 .260
Total 389.082 637
a. Dependent Variable: Consumer Behavioral Intentions
b. Predictors: (Constant), Marketing Promotion
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
32

B Std. Error Beta
1 (Constant) .800 .113 7.049 .000
Marketing Promotion .790 .027 .759 29.359 .000
a. Dependent Variable: Consumer Behavioral Intentions
The table summary presents the value of R-square which is 0.575. This implies that the
model can explain 57.5% of the variability of the dependent variable. The F-stat of the
ANOVA is 861.937 with p-value 0.00 which implies the model is better than the intercept
model which is significant at 5% significance level. Now, the intercept is 0.8 with p-value
0.00 and the coefficient of marketing promotion is 0.790 with the p-value 0.00. Both the
intercept and slope terms are positive and statistically significant at 5% significance level.
This implies that the marketing promotion has a positive impact on consumers' intentions to
use online financial services.
4.7 Research Question 4 Analysis
What is the impact of Technology Acceptance Model (TAM) on consumers' intentions to use
online financial services?
In order to find out the influence of Technology Acceptance Model on consumers'
intentions to use online financial services, the regression analysis is performed and the result
tables are presented below:
Table 10: Regression analysis of Technology Acceptance Model on consumers' intentions to use
online financial services
Model Summary
Mode
l
R R
Square
Adjusted R
Square
Std. Error of
the Estimate
Change Statistics
R Square
Change
F
Change
df1 df2 Sig. F
Change
1 .816a .666 .666 .45174 .666 1270.61
8 1 636 .000
a. Predictors: (Constant), Techonology Acceptance Model (TAM)
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1
Regression 259.294 1 259.294 1270.618 .000b
Residual 129.788 636 .204
Total 389.082 637
33

a. Dependent Variable: Consumer Behavioral Intentions
b. Predictors: (Constant), Techonology Acceptance Model (TAM)
Coefficientsa
Model Unstandardized Coefficients Standardized
Coefficients
t Sig.
B Std. Error Beta
1
(Constant) .702 .096 7.278 .000
Techonology Acceptance
Model (TAM) .821 .023 .816 35.646 .000
a. Dependent Variable: Consumer Behavioral Intentions
The table summary presents the value of R-square which is 0.666. This implies that the
model can explain 66.6% of the variability of the dependent variable. The F-stat of the
ANOVA is 1270.618 with p-value 0.00 which implies the model is better than the intercept
model which is significant at 5% significance level. Now, the intercept is 0.702 with p-value
0.00 and the coefficient of Technology Acceptance Model is 0.821 with the p-value 0.00.
Both the intercept and slope terms are positive and statistically significant at 5% significance
level. This implies that the Technology Acceptance Model has a positive impact on
consumers' intentions to use online financial services.
4.8 Research Question 5 Analysis
What is the most important factor (i.e., IV1, IV2, IV3, and IV4) that impacting the
consumers' intentions to use online financial services
In order to find out the influence of IV1, IV2, IV3, and IV4 on consumers' intentions
to use online financial services, the regression analysis is performed and the result tables are
presented below:
Table 11: Regression analysis of IV1, IV2, IV3, and IV4 on consumers' intentions to use online
financial services
Model Summary
Mode
l R
R
Square
Adjusted R
Square
Std. Error of
the
Estimate
Change Statistics
R Square
Change
F
Change df1 df2
Sig. F
Change
1 .856a .733 .731 .40521 .733 434.149 4 633 .000
34

a. Predictors: (Constant), Techonology Acceptance Model (TAM), Security, Marketing Promotion, Advertising
ANOVAa
Model Sum of Squares df Mean Square F Sig.
1 Regression 285.145 4 71.286 434.149 .000b
Residual 103.937 633 .164
Total 389.082 637
a. Dependent Variable: Consumer Behavioral Intentions
b. Predictors: (Constant), Techonology Acceptance Model (TAM), Security, Marketing Promotion, Advertising
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
t Sig.B Std. Error Beta
1 (Constant) .135 .098 1.383 .167
Security (4) .152 .033 .149 4.557 .000
Advertising (3) .162 .043 .155 3.763 .000
Marketing Promotion (2) .207 .038 .199 5.421 .000
Techonology Acceptance
Model (TAM) (1)
.439 .038 .437 11.639 .000
a. Dependent Variable: Consumer Behavioral Intentions
The table summary presents the value of adjusted R-square which is 0.731. This implies
that the model can explain 73.1% of the variability of the dependent variable. The F-stat of
the ANOVA is 434.149 with p-value 0.00 which implies the model is better than the intercept
model which is significant at 5% significance level. Now, the intercept is 0.135 with p-value
1.67. This implies the intercept term is not statistically significant at 5% significance level.
The coefficient of security is 0.152 with the p-value 0.00. The coefficient of advertising is
0.162 with the p-value 0.00. The coefficient of marketing promotion is 0.207 with the p-value
0.00.The coefficient of Technology Acceptance Model is 0.439 with the p-value 0.00. All the
coefficient of the variables are positive and statistically significant at 5% significance level.
This implies that all the variables has a positive impact on consumers' intentions to use online
financial services. The most important variable is TAM as the coefficient of this variable is
greater than any other coefficient of variable which implies the TAM is the most influential
factor among all the incorporated independent variables.
35

4.9 Results Summary
There are total 638 observations of which 63.5 percent respondents are female and 36.5 percent
respondents are male. Moreover, 90.9 percent respondents are the user of online financial transaction
and 9.1 percent does not do the online financial transaction. Most of the people belongs to the age
group 18-29 years and the least people belongs to the age group 40-59 years. Most of the people
belongs to the income level group RM30,001-RM50,000 and the least people belongs to the income
level group RM60,001 and above. Most of the people belongs to the education level group university
graduate and the least people belongs to the education level group post graduate.
The value of the Cronbach’s alpha is too high which simply indicates that the data set is
reliable to perform the analysis. Now, the slope terms of security is positive and statistically
significant at 5% significance level. This indicates the positive impact of security on consumers'
intentions to use online financial services. The slope terms of advertising is positive and statistically
significant at 5% significance level. This indicates the positive impact of advertising on consumers'
intentions to use online financial services. The slope terms of marketing promotion is positive and
statistically significant at 5% significance level. This indicates the positive impact of marketing
promotion on consumers' intentions to use online financial services. The slope term of Technology
Acceptance Model is positive and statistically significant at 5% significance level. This indicates the
positive impact of Technology Acceptance Model on consumers' intentions to use online financial
services. The regression result including all the independent variables presents that all the coefficient
of the variables are positive and statistically significant at 5% significance level. This implies that all
the variables has a positive impact on consumers' intentions to use online financial services. The most
important variable is TAM as the coefficient of this variable is greater than any other coefficient of
variable which implies the TAM is the most influential factor among all the incorporated independent
variables.
36

Chapter 5
Discussion
5.1 Conclusion
The literature review started from the meaning of online financial services. The literature review
added the concept starting from the Federal Trade Commission Consumer Information (2012), online
financial services known as electronic banking to several research papers and article from peered
review journals. The literature review discusses the findings from these and this chapter discusses the
findings from the statistical analysis performed by the researcher. Finally, it is found that the
statistical analysis supports the literature review with great significance.
5.2 Research Findings Relationship with Previous Literature Reviews
The RQ1 asks to explore the impact of security on consumers’ intentions to use online
financial services. The literature presents that the security issues are more likely to be the most
significant factor in motivating the Chinese consumers to adopt the online financial services (Laforet
& Li, 2005). Moreover, the security and privacy trait and function of the online financial website are
the primary element of trust which are potential enough to influence behavioral intentions of
consumer towards online financial services (Mukherjee & Nath, 2007). In chapter 4, the point 4.4
shows that the slope term of security is positive and statistically significant at 5% significance level.
This indicates the positive impact of security on consumers' intentions to use online financial services.
Hence the finding of the analysis supports the literature review for RQ1.
The RQ2 asks to explore the impact of advertising on consumers’ intentions to use online
financial services. The literature presents that the advertisement is an important factor for all financial
industries in order to promote and inform their existing or future customers about the products and
services offered by the banks. There is a survey conducted by DoubleCLick (2004), justified that there
are more than 60 percent of people who develop positive intention towards the advertisement sender
and also build a positive influence towards the informative advertisement. In chapter 4, the point 4.5
shows that the slope term of advertisement is positive and statistically significant at 5% significance
level. This indicates the positive impact of advertisement on consumers' intentions to use online
financial services. Hence the finding of the analysis supports the literature review for RQ2.
The RQ3 asks to explore the impact of marketing promotions on consumers’ intentions to
use online financial services. The literature presents that the majority of the departmental retail stores
are using rebates system and contest in order to increase the excitement of the consumers toward the
brand as well as offered valuable advertising resources and generating consumers’ intention and
awareness examined by a research study in Singapore (Wah Lee, 2002). In chapter 4, the point 4.6
37

presents that the slope term of marketing promotion is positive and statistically significant at 5%
significance level. This indicates the positive impact of marketing promotion on consumers' intentions
to use online financial services. Hence the finding of the analysis supports the literature review for
RQ3.
The RQ4 askss to explore the impact of Technology Acceptance Model (TAM) on
consumers’ intentions to use online financial services. The literature presents that the TAM yields are
more likely to be have a consistent outcome on the users’ acceptance behavior towards new
technology systems in the workplace environment (Adams et al, 1991; Chin & Gopal, 1995; Lu &
Yeh, 1998; Mathieson, 1991). In chapter 4, the point 4.6 presents that the slope term of Technology
Acceptance Model is positive and statistically significant at 5% significance level. This indicates the
positive impact of Technology Acceptance Model on consumers' intentions to use online financial
services. Hence the finding of the analysis supports the literature review for RQ4.
The most important variable is TAM as the coefficient of this variable is greater than any other
coefficient of variable which implies the TAM is the most influential factor among all the
incorporated independent variables which is shown in the chapter 4 under the heading 4.7.
38

Chapter 6
Recommendation
6.1 Practical Implications
The financial service providers should focus on the middle aged customers who are not using the
online services. However, most of the online service users belongs to the young age group and the
young age group is defined by the customers of age 18-29 years old. So, the financial service provider
can advertise their products and services to the young generation more effectively. This can be done
easily with the help of social media. There are too many social media platforms and it’s easy to reach
out to the people of all ages and especially to the age group of 18-29 years and 30-45 years.
The service providers needs to promote their products and services more efficiently to create
awareness to all the age groups and irrespective of the gender. The analysis shows that the people who
belongs to the income level of RM60,000 and above are only 6% who uses the online services. So, the
financial service providers have to target the other income level groups as the population of the other
three income groups covers almost 94% of the population.
The financial service provider needs to upgrade the technology in order to promote and make the
online transaction process easy to the customers. Most of the people belongs to the educational level
group of university graduate but the percentage of people belongs to the lowers educational level
group of university graduate is 57.2% which is more than the half of the population. Hence the
technology needs to be simple and for that newer and easier technologies are required.
6.2 Theoretical Implications
The future of the financial services that would be sued by the consumers as analyzed would be
highly dependent on the awareness and the connectivity that would be bringing close both the
consumers and the financial sector. As researched advancement in the technological field and better
connectivity with consumer would be serving as the key future of the financial services offered by
various kinds of financial institutions. The three key changes that could be proposed with research
model would be primarily the target group of consumers that would be attracted for the financial
services. The marketing activity deployed by the financial institutions and the last key factor would be
the advancement in the technological factor that would potentially be boosting the overall
performance and the usage of the financial services.
It was a great experience from our side in well researching about the topic and examining the
usage and the application of various kinds of financial services. It was well important and interesting
when in personally researched about the difficulties faced by consumers in general and what can be
39

the key ways that would help us outcome from the associated challenges currently the finance
industry faces.
40

References
Aaker, D.A. (1991), Managing Brand Equity: Capitalizing on the value of a brand name, The
Free Press, New York, NY.
Aaker, D.A. and Biel, A.L (Eds) (1993), Brand Equity and Advertising: Advertising’s role in
building strong brands, Lawrence Erlbaum Associates, Hillsdale, NJ.
Abdul-Gader, 1996. "The Impact of User Satisfaction on Computer-Mediated
Communication Acceptance: A Causal Path Model," Information Resources
Management Journal (IRMJ), IGI Global, vol. 9(1), pages 17-26, January.
Abraham, M.M and Lodish, L.M (1990), “Getting the most out of advertising and
promotions”, Harvard Business Review, May/June, pp.50-60.
Adams, Dennis A., Ryan Nelson, and Peter A. Todd (1992), “Perceived Usefulness, Ease of
Use, and Usage of Information Technology: A Replication,” MIS Quarterly, 16 (2),
227–247.
Agarwal, R., & Prasad, J. (1997). The Role of Innovation Characteristics and Perceived
Voluntariness in Acceptance of Information Technologies. Decision Science, 28(3),
557-582.
Agarwal, Ritu, and Jayesh Prasad (1999), “Are Individual Differences Germane to the
Acceptance of New Information Technologies?” Decision Sciences, 30 (2), 361–391.
Ajzen, Icek, and B.L. Driver (1992), “Contingent Value Measurement: On the Nature and
Meaning of Willingness to Pay,” Journal of Consumer Psychology, 1 (4), 297–316.
Akinci, S., Aksoy, Ş., & Atilgan, E. (2004). Adoption of Internet banking among
sophisticated consumer segments in an advanced developing country. International
Journal of Bank Marketing, 22(3), 212-232. doi:10.1108/02652320410530322
Aladhi, N. (2009). Central bank of Yemen.Sana’a, Republic of Yemen.
Ali, Mohamed Salih. (2017). The effect of E-Banking orientation on customer service
quality KSA banks customers' perspective. International Journal of Civil Engineering
and Technology. 8. 546-63.
Almaghrabi, Talal & Dennis, Charles. (2011). What drives consumers' continuance intention
to e-shopping? Conceptual framework and managerial implications in the case of
41

Saudi Arabia. International Journal of Retail & Distribution Management. 39. 899-
926. 10.1108/09590551111183308.
American Association of Advertising Agencies.
Amin, M. (2016). Internet banking service quality and its implication on e-customer
satisfaction and e-customer loyalty. International Journal of Bank Marketing, 34(3),
280-306.
Bae, S. W., & Choi, H. Y. (2001).The impact of magazine credibility and the degree of
information processing on advertising effectiveness. Journal of Advertising, 5461-
5466.
Bagozzi, R.P., & Warsaw, P.R. (1989). User Acceptance of Computer Technology: A
Comparison of Two Theoretical Models. Management Science, 35(8), 983-1003.
Barkhordari, M., Nourollah, Z., Mashayekhi, H., Mashayekhi, Y., & Ahangar, M. S. (2017).
Factors influencing adoption of e-payment systems: an empirical study on Iranian
customers. Information systems and e-business management, 15(1), 89-116.
Bartos, R. & Dunn, T. (1974). Advertising and consumers: New perspectives. New York:
Bauer, R. A. & Greyser, S. A. (1968). Advertising in America: The consumer view. Boston:
Baumeister, Roy Frank (2002), “Yielding to Temptation: Self- Control Failure, Impulse
Purchasing, and Consumer Behavior,” Journal of Consumer Research, 28 (4), 670–677.
Bawa, K and Shoemaker, R.W. (1987), “The effects of a direct mail coupon on brand choice
behaviour”, Journal of Marketing Research, Vol. 24, November, pp.370-6.
Bawa, K and Shoemaker, R.W. (1989), “Analyzing incremental sales from a direct mail
coupon promotion”, Journal of Marketing Research, Vol. 53, July, pp.66-76.
Behavior, 24 (1), 48-65.
Blattberg, R.C and Neslin, S.A, (1990), Sales Promotion: Concepts, Methods and Strategies,
Prentice-Hall, Englewood Cliffs, NJ.
Bradley, L., & Stewart, K. (2002). A Delphi study of the drivers and inhibitors of Internet
banking. International Journal of Bank Marketing, 20(6), 250-260.
doi:10.1108/02652320210446715
Campbell, L. and Diamond, W.D. (1989), “Effects of framing on the perception of sales
promotions”, in Rotzoll, K.B. (Ed.), Proceedings of the 1989 Conference of the
American Academy of Advertising, RC51-RC56.
42

Chan, R.Y. (2004). Consumer responses to environmental advertising in China.Marketing
Intelligence & Planning, 22(4), 427-437.
Chandrashekaran, R. and Grewal, D. (2003), “Assimilation of advertised reference prices:
the moderating role of involvement”, Journal of Retailing, Vol. 79 No. 1, pp. 53-62.
Cheng, D., Liu, G., & Qian, C. (2008). On Determinants of User Acceptance of Internet
Banking: A Theoretical Framework and Empirical Study. 2008 IEEE Symposium on
Advanced Management of Information for Globalized Enterprises (AMIGE).
doi:10.1109/amige.2008.ecp.20
Conlon, T.J (1980), “Sweepstakes rank as tops”, Advertising Age, 6 October, pp.56-8.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer
technology: A comparison of two theoretical model. Management Science, 35(8), 982}1003
Davis, F.D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of
information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319-40.
Davis, Fred D. (1986), “Technology Acceptance Model for Empirically Testing New End-
User Information Systems: Theory and Results,” Ph.D. dissertation, Massachusetts Institute
of Technology, Cambridge. ——— (1989), “Perceived Usefulness, Perceived Ease of Use
and User Acceptance of Information Technology,” MIS Quarterly, 14(3), 319–340.
Devlin, J., Ennew, C., McKechnie, S., & Smith, A. (2007). A study of time limited price
promotions. Journal of Product & Brand Management, 16(4), 280–285.
doi:10.1108/10610420710763967
Dodds, W., Monroe, K.B. and Grewal, D. (1991), “The effects of price, brand, and store
information on buyers’ product evaluations”, Journal of Marketing Research, Vol. 28
No. 3, pp. 307-19.
Donthu, N., Cherian, J., & Bhargava, M. (1993). Factors influencing recall of outdoor
advertising. Journal of Advertising Research, 33(3), 64–72.
DoubleClick (2002), “DoubleClick’s dartmail study reveals 88% of consumers have made
purchases as a result of permission based email”, available at: www.doubleclick.com/us/
corporate/presskit (accessed May 23, 2019).
DoubleClick (2004), “DoubleClick’s consumer email study 2004”, available at:
www.emailgarage.com/attachments (accessed June 12, 2019).
43

Ducoffe, R.H. (1996), “Advertising value and advertising on the web”, Journal of
Advertising
Dutta-Bergman, M.J. (2006), “The demographic and psychographic antecedents of attitude
Ennew, C., Watkins, T., & Wright M. (1995).Marketing financial services. Oxford,
Heinemann.
Fishbein, Martin, and Icek Ajzen (1975), Belief, Attitude, Intention] and Behavior: An
Introduction to Theory and Research, Reading, MA: Addison-Wesley.
framework. In L. Alwitt & A. Mitchell (Eds.), Psychological processes and advertising
effects: Theory, research, and applications (pp. 45–63). Hillsdale, NJ: Lawrence Erlbaum
Associates.
Francisco, L. C., Francisco, M. L., & Juan, S. F. (2015). Payment systems in new electronic
environments: Consumer behavior in payment systems via SMS. International
Journal of Information Technology & Decision Making, 14(02), 421-449.
Frini, S., & Limayem, M. (2004). Les de l’achat dur Internet : revue de la littératureet
proposition de modèle». Paper presented at the conference «E-MARKETING».
Gefen, David & Straub, Detmar. (1997). Gender Differences in the Perception and Use of E-
Mail: An Extension to the Technology Acceptance Model. MIS Quarterly. 21. 389-400.
10.2307/249720.
Gefen, David, Elena Karahanna, and Detmar W. Straub (2003), “Trust and TAM in Online
Shopping: An Integrated Model,” MIS Quarterly, 27 (1), 51–90.
Gerrard, P., & Barton Cunningham, J. (2003). The diffusion of Internet banking among
Singapore consumers. International Journal of Bank Marketing, 21(1), 16-28.
doi:10.1108/02652320310457776
Grabner‐Kräuter, S., & Faullant, R. (2008). Consumer acceptance of internet banking: the
influence of internet trust. International Journal of Bank Marketing, 26(7), 483-504.
doi:10.1108/02652320810913855
Grewal, D. and Compeau, L.D. (1992), “Comparative price advertising: informative or
deceptive?”, Journal of Public Policy and Marketing, Vol. 11 No. 1, pp. 52-62.
Grewal, D., Monroe, K.B. and Krishnan, R. (1998), “The effects of price-comparison
advertising on buyers’ perceptions of acquisition value, transaction value and
behavioral intentions”, Journal of Marketing, Vol. 62 No. 2, pp. 46-59.
44

Gupta, S. (1988), “Impact of sales promotions on when, what and how much to buy”,
Journal of Marketing Research, Vol. 25, November, pp.342-55.
Haque, A., Ismail, A. Z., & Daraz, A. H. (2009). Issues of e-banking transaction: An
empirical investigation on Malaysian customers perception. Journal of Applied
Sciences, 9(10), 1870-1879. doi:10.3923/jas.2009.1870.1879
Harris, B.F., & Mills, M.K. (1981). The acceptance of technological change in retailing: the
case of scanners and item price removal, in The Marketing Environment: New Theories and
Applications, series No.47, eds. Kenneth Bernhardt et al., Chicago: American Marketing
Assocication, 66-69.
Harvard.
Herr, P.M., Kardes, F.P. and Kim, J. (1991). Effects of word-of-mouth and product-attribute
information on persuasion: an accessibility-diagnosticity perspective. Journal of Consumer
Research, 17, 454-462.
Hettiarachchi, H. (2014). Factors affecting to customer adoption of internet banking.
Kelaniya Journal of Management, 2(2), 68. doi:10.4038/kjm.v2i2.6551
Hill, R. J., Fishbein, M., & Ajzen, I. (1977). Belief, Attitude, Intention and Behavior: An
Introduction to Theory and Research. Contemporary Sociology, 6(2), 244.
doi:10.2307/2065853
Ho, S. S., & Ng, V. T. (1994). Customers′ Risk Perceptions of Electronic Payment Systems.
International Journal of Bank Marketing, 12(8), 26-38.
doi:10.1108/02652329410069029
Hoppe, R., Mugera, P., & Newman, P. (n.d.). National Environment and Internet Banking
Adoption. Advanced Topics in Global Information Management, Volume 4.
doi:10.4018/9781591404682.ch014
Hsu, C. L., Chang, K. C., & Chen, M. C. (2012). The impact of website quality on customer
satisfaction and purchase intention: perceived playfulness and perceived flow as
mediators. Information Systems and e-Business Management, 10(4), 549-570.
Hutchinson, D., & Warren, M. (2003). Security for Internet banking: a framework. Logistics
Information Management, 16(1), 64-73. doi:10.1108/09576050310453750
45

Im, S., Bayus, B. L., & Mason, C. H. (2003). An Empirical Study of Innate Consumer
Innovativeness, Personal Characteristics, and New-Product Adoption Behavior.
Journal of the Academy of Marketing Science, 31(1), 61-73.
doi:10.1177/0092070302238602
Jagoda, D. (1984), “Sweepstakes: It’s not what you give but what you get”, Marketing
Communications, Vol.49, pp.27-31.
James, W. L. & Kover, A. (1992). Do overall attitudes toward advertising affect involvement
with specific advertisements? Journal of Advertising Research, 32(5), 78–83.
Jayawardhena, C., & Foley, P. (2000). Changes in the banking sector – the case of Internet
banking in the UK. Internet Research, 10(1), 19-31.
doi:10.1108/10662240010312048
Juan Carlos Roca, Juan José García, Juan José de la Vega, (2009) "The importance of
perceived trust, security and privacy in online trading systems", Information
Management & Computer Security, Vol. 17 Issue: 2, pp.96-113,
https://doi.org/10.1108/09685220910963983
Kahneman, D. and Tversky, A. (1979), “Prospect theory: an analysis of decision under risk”,
Econometrica, Vol.47, pp.263-91.
Kotler, P (2000), Marketing Management, Prentice-Hall International, London.
Kotlet, P, Ang, S.H., Leong, S.M. and Tan, C.T. (1999), Marketing Management: An Asian
Perspective, 2nd ed., Prentice-Hall, Singapore.
KUMAR, M. (2016). CONSUMER BEHAVIOR AND SATISFACTION IN E-
COMMERCE: A COMPARATIVE STUDY BASED ON ONLINE SHOPPING OF
SOME ELECTRONIC GADGETS. CLEAR International Journal of Research in
Commerce & Management, 7(7).
Kumar, R. (2019). Research methodology: A step-by-step guide for beginners. Sage
Publications Limited.
Lallmahamood M., (2007) An Examination of Individual‟s Perceived Security and Privacy
of the Internet in Malaysia and the Influence of This on Their Intention to Use E-
Commerce: Using An Extension of the Technology Acceptance Model, Journal of
Internet Banking and Commerce December 2007, vol. 12, no.3 pp. 1-26
46

Lassar, W. M., Manolis, C., & Lassar, S. S. (2005). The relationship between consumer
innovativeness, personal characteristics, and online banking adoption. International
Journal of Bank Marketing, 23(2), 176-199. doi:10.1108/02652320510584403
Lee, J. (2003). Factors affecting intention to use online financial services (Doctoral
dissertation, The Ohio State University). Retrieved from https://etd.ohiolink.edu/
Lian, J.W. & Lin, T.M., 2008. Effects of consumer characteristics on their acceptance of
Liao, Z. and Cheung, M.T. (2002), “Internet-based e-banking and consumer attitudes: an
empirical study”, Information and Management, Vol. 39, pp. 283-95.
Liu, J. N., & Zhang, E. Y. (2014). An investigation of factors affecting customer selection of
online hotel booking channels. International Journal of Hospitality Management, 39,
71-83.
Lloret, A., (2016). Modeling corporate sustainability strategy. Journal of Business
Research, 69(2), pp.418-425.
Lutz, R. J. (1985). Affective and cognitive antecedents of attitude toward the ad: A
conceptual
MacKenzie, S. B. & Lutz, R. J. (1989). An empirical examination of the structural
antecedents of attitude toward the ad in an advertising pretesting context. Journal of
Marketing, 53(2), 48–65.
MacKenzie, S. B., Lutz, R. J., & Belch, G. E. (1986). The role of attitude toward the ad as a
mediator of advertising effectiveness: A test of competing explanations. Journal of Marketing
Research, 23(2), 130–143.
Mackey, A. & Gass, S.M., (2015). Second language research: Methodology and design.
Routledge.
Malaysia Population. (2019). Retrieved July 11, 2019, from
http://worldpopulationreview.com/countries/malaysia-population/
Marczyk, G., DeMatteo, D. & Festinger, D., (2005). Essentials of research design and
methodology. John Wiley & Sons Inc.
Mas-Colell, A., Whinston, M.D. and Green, J.R. (2995), Microeconomic Theory, Oxford
University Press, New York, NY.
Mathieson, Kieran (1991), “Predicting User Intentions: Comparing Technology Acceptance
Model with the Theory of Planned Behavior,” Information Systems Research, 2 (3), 173–191.
47

Mehta, A. & Purvis, S. C. (1995). When attitudes toward advertising in general influence
advertising success. Paper presented at the 1995 Annual Conference of the American
Academy of Advertising, Waco, TX.
Mehta, A. (2000). Advertising attitudes and advertising effectiveness. Journal of Advertising
Mitchell, A. & Olson, J. C. (1981). Are product attribute beliefs the only mediator of
advertising effects on brand attitudes? Journal of Marketing Research, 18(3), 318–332.
Nui Polatoglu, V., & Ekin, S. (2001). An empirical investigation of the Turkish consumers’
acceptance of Internet banking services. International Journal of Bank Marketing,
19(4), 156-165. doi:10.1108/02652320110392527
O'Cass, Aron & Fenech, Tino. (2003). Web Retailing Adoption: Exploring the Nature of
Internet Users Web Retailing Behaviour. Journal of Retailing and Consumer
Services. 10. 81-94. 10.1016/S0969-6989(02)00004-8.
Oly Ndubisi, N., & Sinti, Q. (2006). Consumer attitudes, system's characteristics and internet
banking adoption in Malaysia. Management Research News, 29(1/2), 16-27.
doi:10.1108/01409170610645411
Oly Ndubisi, N., & Sinti, Q. (2006). Consumer attitudes, system's characteristics and internet
banking adoption in Malaysia. Management Research News, 29(1/2), 16-27.
doi:10.1108/01409170610645411
Oly Ndubisi, N., Gupta, O. K., & Ndubisi, G. C. (2005). The Moguls' Model of Computing:
Integrating the Moderating Impact of Users' Persona into the Technology Acceptance
Model. Journal of Global Information Technology Management, 8(1), 27-47.
doi:10.1080/1097198x.2005.10856389
online shopping: Comparisons among different product types. Computers in Human
Peffers, K., Tuunanen, T., Rothenberger, M.A. & Chatterjee, S., (2007). A design science
research methodology for information systems research. Journal of management
information systems, 24(3), pp.45-77.
Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance
of online banking: an extension of the technology acceptance model. Internet
Research, 14(3), 224-235. doi:10.1108/10662240410542652
48

Piron, Francis (1991), “Defining Impulse Purchasing,” in Advances in Consumer Research,
vol. 18, Rebecca Holman and Michael Solomon, eds., Provo, UT: Association for Consumer
Research, 509–514.
Poon, W. (2007). Users' adoption of e‐banking services: the Malaysian perspective. Journal
of Business & Industrial Marketing, 23(1), 59-69. doi:10.1108/08858620810841498
Quinlan, C., Babin, B., Carr, J. & Griffin, M., (2019). Business research methods. South
Western Cengage.
R. Vijayasarathy, Leo. (2004). Predicting consumer intentions to use on-line shopping: The
case for an augmented technology acceptance model. Information & Management.
41. 747-762. 10.1016/j.im.2003.08.011.
Ranganathan, C & Ganapathy, Shobha. (2002). Key dimensions of business-to-consumer
Web sites. Information & Management. 39. 457-465. 10.1016/S0378-
7206(01)00112-4.
Research, 40(3), 67–72.
Research, Vol. 36 No. 5, pp. 21-35.
Rettie, R., Grandcolas, U., &Deakins, B. (2003). Text message advertising: dramatic effect
on purchase intentions. Marketing Communication and Promotion Track.
Sathye, M. (1999). Adoption of Internet banking by Australian consumers: an empirical
investigation. International Journal of Bank Marketing, 17(7), 324-334.
doi:10.1108/02652329910305689
Sathye, M. (1999). Adoption of Internet banking by Australian consumers: an empirical
investigation. International Journal of Bank Marketing, 17(7), 324-334.
doi:10.1108/02652329910305689
Schiffman, L.G. & Kanuk, L.L. (1997). Consumer Behavior. Prentice Hall. NJ.
Shah, A. M., Zahoor, S. Z., & Qureshi, I. H. (2019). Social Media and Purchasing Behavior:
A Study of the Mediating Effect of Customer Relationships. Journal of Global
Marketing, 32(2), 93-115.
Shamout, M. D. (2016). The impact of promotional tools on consumer buying behavior in
retail market. International Journal of Business and Social Science, 7(1), 75-85.
49

Silvera, D. H. & Austad B., (2004). Factors Predicting the Effectiveness of Celebrity
Endorsement Advertisements. European Journal of Marketing, 38 (11/12), p. 1509-
1526.
Singh, S., & Srivastava, S. (2019). Engaging consumers in multichannel online retail
environment: A moderation study of platform type on interaction of e-commerce and
m-commerce. Journal of Modelling in Management, 14(1), 49-76.
Sohail, M. S., & Shanmugham, B. (2003). E-Banking and Customer Preferences in
Malaysia: An Empirical Investigation. SSRN Electronic Journal.
doi:10.2139/ssrn.2650354
Soroka, S., Bodet, M.B., Young, L., & Andrew, B. (2008).Campaign news and
voteintentions.Public opinion and parties, McGill University. Canada.
Taylor, S., & Todd, P. A. (1995). Understanding Information Technology Usage: A Test of
Competing Models. Information Systems Research, 6(2), 144-176.
doi:10.1287/isre.6.2.144
Tellis, G.J. (1998), Advertising and sales promotion strategy, addison-Wesley, Reading, MA.
Teng, L., & Laroche, M. (2007). Building and testing models of consumer purchase intention
in competitive and multicultural environments. Journal of Business Research, 60(3), 260-268.
Teo, T. S., & Lim, V. K. (1998). Factorial dimensions and differential effects of gender on
perceptions of teleworking. Women in Management Review, 13(7), 253-263.
doi:10.1108/09649429810237105
Teo, T.S. (2002). Attitudes Toward Online Shopping and the Internet. Behavior &
Information Technology, 21(4), 259-271.
toward advertising”, Journal of Advertising Research, Vol. 46 No. 1, pp. 102-111.
Tsai, J., Egelman, S.; Cranor, L., & Acquisti, A. (2007). The effect of online privacy
information on purchasing behavior: An experimental study. Carnegie Mellon University.
Wah Lee, C. (2002). Sales promotions as strategic communication: the case of Singapore.
Journal of Product & Brand Management, 11(2), 103–114.
doi:10.1108/10610420210423473
50

Wang, L., Law, R., Guillet, B. D., Hung, K., & Fong, D. K. C. (2015). Impact of hotel
website quality on online booking intentions: eTrust as a mediator. International
Journal of Hospitality Management, 47, 108-115.
Wang, Y. Lo, H.P. Chi, R. and Yang, Y (2004), “An integrated framework for customer
value and customer-relationship-management performance: a customer-based
perspective from China”, Managing Service Quality, V. 14, No. 2/3 pp 169-182.
Wang, Y., Wang, Y., Lin, H., & Tang, T. (2003). Determinants of user acceptance of
Internet banking: an empirical study. International Journal of Service Industry
Management, 14(5), 501-519. doi:10.1108/09564230310500192
Yang, Q., Pang, C., Liu, L., Yen, D. C., & Tarn, J. M. (2015). Exploring consumer perceived
risk and trust for online payments: An empirical study in China’s younger generation.
Computers in Human Behavior, 50, 9-24.
Yaokumah, W., Kumah, P., & Okai, E. S. A. (2017). Demographic influences on e-payment
services. International Journal of E-Business Research (IJEBR), 13(1), 44-65.
Yoon, S. (2002). The antecedents and consequences of trust in online-purchase
decisions. Journal of Interactive Marketing, 16(2), 47-63. doi:10.1002/dir.10008
Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The Behavioral Consequences of
Service Quality. Journal of Marketing, 60(2), 31–46.
doi:10.1177/002224299606000203
Zendehdel, M., Paim, L. H., & Osman, S. B. (2015). Students’ online purchasing behavior in
Malaysia: Understanding online shopping attitude. Cogent Business & Management,
2(1), 1078428.
51

Appendix
Appendix 1 - Literature Review Summary
Findings Authors
Online financial services defined as electronic banking or
Electronic Fund Transfer (EFT)
The Federal Trade
Commission
Consumer
Information (2012)
Online financial services is defined as a mobile electronic and
remote distribution channel to conduct or deliver the financial
activities on a virtual environment.
Bradley (2002) and
Reiser (1997)
Consumers’ intention can be defined as the interest of individuals Ajzen (1991)
Consumers’ behavioural intention can be defined as the strength of
the expected consumers’ intention to pursue or support the decision
of use
Davis (1986)
Behavioural intention is related to the attitude of an individual’s
feelings about performing an action.
Fishbein Ajzen
(1975)
Consumers’ intentions has positive relationship with new product
adoption behaviour.
Foxall & Goldsmith
(1988) and Manning
et al (1995)
Personal intention in terms of individuals’ willingness has positive
relationship with the adoption of new product experiences.
Walton & Leavitt
(1975)
Individual has greater intention to use mobile banking has positive
attitude towards using online transaction systems.
Lee (2003)
Past transaction behaviour, direct experiences, advertisement and
word-of-mouth information has positive relationship with the
intention to use.
Schiffman & Kanuk,
(1997)
Consumers’ perceptions on security has positive relationship with
the intention to purchase online.
Flavia´n & Guinalı´u
(2006)
Attitude to practical use has positively affect the consumers’
intention toward using a product or service.
Davis (1986);
Mathieson (1991)
and Venkatesh &
52

Davis (1994)
The researchers defined “security” as a menace which brought
circumstance or activity with latent to cause economic tribulation to
data information or network resources in the form of disclosures,
devastation, and modification of data information, fraud or denial
of services
Kalakota &
Whinston (1997)
Security is one of the primary barriers for the online financial
services to growth as the personal data information could be used
for fraudulent activity
Jarvenpaa et al
(1999); Gefen (2000)
and Wang et al
(1998)
The consumers’ subjective security perception has given bigger
impact towards the acceptance of e-commerce adoption rather than
the objective security of the mobile transaction channel.
Klang (2001) and
Grabner-Kra¨uter &
Kaluscha (2003)
Security issue tend to be the most significant element that
motivated Chinese consumers toward adoption of online financial
services.
Laforet & Li (2005)
Security and privacy trait and function of the online financial
website have positive relationship with the consumers’ behavioural
intentions toward online financial services.
Mukherjee & Nath
(2007)
Security factor has positive influence towards the consumers’
intention and user acceptance to use.
Poon (2007); Sathye
(1999) and Liao &
Cheung (2002)
Security has a positive effect towards the consumers’ intention to
conduct online transaction.
Ranganathan &
Ganapathy (2002)
and Kim et al (2008)
The term “advertising” defined as a way which a business owner
provides information details to promote their activities, products,
services, pricing and delivery channel in order to attract people
from existing or future consumers.
Ennew et al (1995)
Advertisement is one of the important factor for all financial
industries to promote and informing their existing or future
customers about the products and services offered by the banks.
Chan (2004) and
Adhi (2009)
53

Advertisement help to create awareness and knowledge information
on the benefits of the consumers in the society.
Rettie et al (2003)
Consumers with more positive intentions and perceptions toward
advertisements generally tend to spend more time to explore at the
ads as well as recall more advertisements in future.
James & Kover
(1992) and Donthu
et al (1993)
There are more than 60% of people who develop positive intention
towards the advertisement sender as well as build a positive
influence towards the informative advertisement.
DoubleCLick (2004)
Price promotions aimed to enhance consumers’ perceptions of
value and purchase
Chandrashekaran
and Grewal (2003)
and Grewal et al
(1998)
Marketing promotions can be separate in two different types, such
as price-oriented and non-priced-oriented promotion
Kotler et al (1999)
Marketing promotions has positive relationship towards users’
intentions on the product and services offered.
Bawa & Shoemaker
(1989); Gupta (1988)
and Blattberg &
Neslin (1990)
Non-priced-oriented promotions has positively affect the
consumers’ attitudes and intention to use the products and services.
Conlon (1980);
Jagoda (1984) and
Aaker (1991)
Non-price-oriented promotions are often perceived as gaining
customer attraction, view, loyalty and brand image whereas the
price-oriented promotions are often used for minimizing losses.
Campbell &
Diamond (1989)
Rebates system and contest can increase the excitement of the
consumers toward the brand and also offered valuable advertising
resources and generating consumers’ intention and awareness.
Wah Lee (2002)
The acceptance of IT can be firmed by two key factors, such as
perceived usefulness and perceived ease of use which affect a
users’ intention to use a technology.
Davis et al (1989)
The researcher has use the Technology Acceptance Model (TAM)
to carry out research in order to investigate the number of time an
Baumeister (2002)
54

individual performed online transaction.
The researchers used TAM in their research study to examine the
perception on the way people deems towards his or her behaviour
and the motivation to carry out with the expectations of these
referents
Fishbein & Ajzen
(1957)
TAM yields are more likely to be have a consistent outcome on the
users’ acceptance behaviour towards new technology systems in
the workplace environment.
Adams et al (1991);
Chin & Gopal
(1995); Lu & Yeh
(1998) and
Mathieson (1991)
TAM have been extended in several research studies to access the
variables, such as Internet availability, speed and cost would affect
the users’ behaviour and intentions to use the technology.
Agarwal & Prasad
(1997); Teo (2002);
Guimararaes &
Davis (1995) and
Gefen & Straub
(1997)
Appendix 2 - Cover Letter for Survey Questionnaires
55

Appendix 3 – Survey Overview Table
56

Appendix 4 - Survey Questionnaires
57

58