Thesis - Duy Nguyen v.final

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

------------------------------

Nguyen Khac Duy

FACTORS AFFECTING BEHAVIORAL INTENTIONS TOWARD MOBILE BANKING USAGE:

A STUDY OF BANKING CUSTOMERS IN HO CHI MINH CITY

ID: 60340102

MASTER OF BUSINESS (Honours)

SUPERVISOR: Dr. Nguyen Thi Nguyet Que

Ho Chi Minh City – Year 2012

[2]

ACKNOWLEDGEMENT

At the first of my thesis, I would like to thank all those people who made this thesis possible and an unforgettable experience for my studying.

Foremost, I would like to express my sincere gratitude to my supervisor, Dr. Nguyen Thi Nguyet Que, for the continuous support of my MBUS study and research, for her patience, motivation, enthusiasm, and immense knowledge. Her guidance helped me in all the time of research and writing of this thesis.

Besides my supervisor, I would like to thank the rest of my thesis committee: Prof. Nguyen Dong Phong, Prof. Nguyen Dinh Tho, and Dr. Tran Ha Minh Quan, for their encouragement, insightful comments, and hard questions.

I thank my classmates in ISB MBUS 2010 encourage and support me complete this thesis. Completing this work would have been all the most difficult were it not for the support and friendship provided by the members of University of Economics Ho Chi Minh City - International School of Business.

Last but not the least; I would like to thank my family. I must express my gratitude to Nguyen Thi Hong Hiep, my wife, for her continued support and encouragement. I also wish thank all those people who spent through their time and generous support made this thesis project a dream come true.

Ho Chi Minh City, December 18, 2012

NGUYEN KHAC DUY

[3]

ABSTRACT

Mobile phones with banking technology are becoming more readily available in Vietnam. Similarly, many financial institutions and mobile phone service providers are teaming up to provide several banking services to customers via the mobile phone. However, the number of people who choose to adopt or use such technologies is still relatively low. Therefore, there is a need to assess the acceptance of such technologies to establish factors that hinder or promote customer‟s intention to use mobile banking (MB). Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB) are the base models in order to investigate the customers‟ intention to use mobile banking services in Ho Chi Minh City (HCMC). A questionnaire with five-point Likert scale is survey to 400 target respondents. This research combines the variables

(1) “perceived usefulness”, (2) “perceived ease of use”, (3) “attitude”, (4) “subjective norm”, and (5) “Perceived behavioral control” in a proposed model to reflect consumer‟s intention to use mobile banking. Results indicate that perceived usefulness, perceived ease of use, subjective norm, and perceived behavioral control are significant with respect to the customer‟s intention to use mobile banking services. In constrast with previous studies, attitude was not significant in explaining mobile banking adoption. In summary, perceived behavioral control contributes the most in explaining the factor of intention to use mobile banking. The results of the data analysis contributes to the body of knowledge by demonstrating that the above factors are critical in intention to use mobile banking in a developing country context. The implications of the results form a good basis for providing practical recommendations to leaders of commercial banks, and directions for further work.

[4]

TABLE OF CONTENTS

ACKNOWLEDGEMENT ..................................................................................... 2 ABSTRACT ........................................................................................................... 3 LIST OF FIGURES ................................................................................................ 7 LIST OF TABLES ................................................................................................. 7 CHAPTER 1: INTRODUCTION .......................................................................... 8 1.1. Research background ................................................................................... 8 1.2. Overview of electronic banking market in Ho Chi Minh City .................... 9 1.2.1. E-banking services ................................................................................ 9 1.2.2. Advantages of e-banking services ....................................................... 11 1.2.3. Difficulties in implementing e-banking services ................................

12 1.3. Problem statement ...................................................................................... 13 1.4. Research objective ..................................................................................... 14 1.5. Research scopes and limitations ................................................................ 15 1.6. Research implications ................................................................................ 15 1.7. Thesis structure .......................................................................................... 16 1.8. Summary .................................................................................................... 17 CHAPTER 2: LITERATURE REVIEW ............................................................. 18 1.1. Theoretical background ............................................................................. 18 1.1.1. Technology Acceptance Model ........................................................... 18 1.1.2.

Theory of Planned Behavior ............................................................... 19 1.2. Research model and hypotheses ................................................................ 20 1.3. Summary .................................................................................................... 24 CHAPTER 3: METHODOLOGY ....................................................................... 25

[5]

3.1. Research design.......................................................................................... 25 3.2. Development of questionnaire ................................................................... 26 3.2.1. Measurement scales............................................................................. 26

3.2.1.1. Measure of Perceived usefulness and Perceived ease of use ....... 26

3.2.1.2. Measure of Attitude ...................................................................... 27

3.2.1.3. Measure of Subjective norms ....................................................... 28

3.2.1.4. Measure of Perceived Behavioural Control ................................. 29

3.2.1.5. Measure of Intention to use mobile banking ................................ 29

3.2.2. Draft questionnaire .............................................................................. 30 3.3. Pilot study .................................................................................................. 30 3.4. Sample method ........................................................................................... 31 3.5. Questionnaire administration ..................................................................... 32 3.6. Data analysis methods ................................................................................ 32 3.6.1. Reliability ............................................................................................ 32 3.6.2. Exploratory Factor Analysis (EFA) .................................................... 33 3.6.3. Multiple regression analysis ................................................................ 33 3.7. Summary .................................................................................................... 34

CHAPTER 4: DATA ANALYSIS AND RESULTS .......................................... 35 4.1. Descriptive analysis ................................................................................... 35 4.2. Measure assessment ................................................................................... 36 4.2.1. Reliability analysis .............................................................................. 36 4.2.2. Exploratory Factor Analysis (EFA) .................................................... 38 4.3. Hypotheses testing ..................................................................................... 41 4.4. Testing the effect of demographic variables .............................................. 44 4.5. Summary .................................................................................................... 44

[6]

CHAPTER 5: CONCLUSION

5.1. Overview

46

46

5.2. Main findings 46

5.3. Practical implications 47

5.4. Theoretical implications

5.5. Limitations

48

APPENDICES

52

47

APPENDIX 1: QUESTIONNAIRE

52

APPENDIX 2: DESCRIPTIVE STATISTICS

56

APPENDIX 3: CRONBACH‟S ALPHA RELIABILITY ANALYSIS

APPENDIX 4: INTER-ITEM CORRELATION MATRIX

61

APPENDIX 5: THE FIRST-TIME RUNNING FACTOR ANALYSIS –

58

EIGENVALUES (FOR INDEPENDENT VARIABLES)

62

APPENDIX 6: THE FIRST TIME RUNNING – FACTOR LOADING

63

APPENDIX 7: THE SECOND TIME RUNNING – EIGENVALUES

65

APPENDIX 8: THE SECOND TIME RUNNING – FACTOR LOADING 66

APPENDIX 9: THE FIRST TIME RUNNING – EIGENVALUES AND

FACTOR LOADING (FOR DEPENDENT VARIABLE)

APPENDIX 10: MULTIPLE REGRESSION

67

68

APPENDIX 11: CHARTS OF TESTING HYPOTHESES

69

APPENDIX 12: AFFECTING OF DEMOGRAPHIC VARIABLES 71

[7]

LIST OF FIGURES

Figure 1: Technology Acceptance Model 14

Figure 2: Theory of Planned Behaviour 16

Figure 3: The proposed research model with hypotheses

17

Figure 4: Research process 22

LIST OF TABLES

Table 1: Figure of e-banking services of 15 commercial banks in HCMC

Table 2: Scale of Perceived usefulness and Perceived ease of use

Table 3: Scale of Attitude 28

Table 4: Scale of Subjective norms

29

27

10

Table 5: Scale of Perceived Behavioural Control29

Table 6: Scale of Intention to use mobile banking

30

Table 7: Cronbach‟s alpha reliability coefficient 33

Table 8: Descriptive statistic of respondent‟s characteristics

Table 9: Reliability analysis for each factor

38

Table 10: Key dimensions, items 41

Table 11: R Square Value (R2)

43

Table 12: ANOVA 43

Table 13: Beta Coefficient 43

Table 14: Creating and recoding variables

45

36

[8]

CHAPTER 1: INTRODUCTION

The introduction chapter identifies the research background, present the problem statement, and introduce the research objectives as well as the scope of research. Futhermore, the research implications is also outline.

1.1. Research background

Mobile banking is an innovative service, which has been perpetuated by the development and diffusion of the mobile communication technology. Mobile banking is defineded as “the financial services delivered via mobile networks and performed on a mobile phone” (Bangens & Soderberg, 2008, p.26). This service provides much convenience and promptness to the banks‟ customers along with cost savings. Many banks are interested in expanding their market through mobile services.

Traditionally, the most widespread method of conducting banking transactions has been through offline retail banking. Mobile banking, however, is the recent trend in banking transition and holds a bright future that is promising over and above the one brought by electronic banking (e-banking). Mobile banking provides personalized, anytime - anywhere banking services thus making it the future of banking. In the last several years, several commercial banks in Vietnam have introduced and diffused some mobile banking systems.

The world economy is going through a crisis period. Consumers of today are highly sophisticated and their need for personalized service is ever increasing by the day. The digital age customers now require banking services serve to them anywhere they are.

As an emerging technological innovation, especially in the developing countries, mobile banking is yet to gain acceptance on a wide scale and adoption level is marginally insignificant (Amin, 2007, p.31). Hence, the need to understand the factors influencing intention to use mobile banking services is indispensable.

[9]

1.2. Overview of electronic banking market in Ho Chi Minh City

So far, all commercial banks have built core-banking system, which connects online system. The commercial banks have same level of investment in technology, because the same solutions and network infrastructure. Therefore, the commercial banks can launch e-banking services similar to each other.

1.2.1. E-banking services

Currently, the commercial banks in HCMC have developed banking services via e-banking with convenient. Many new payment services and value-added utilities was, promoted for payment services, create competitive advantage among the banks. Therefore, the banks have focused on technology, infrastructure effectively to serve the best payment services via e-banking, with the expanding scope to serve businesses and individuals customer. Practical figures in HCMC shows, to the end of 2011, there were 111,861 customers are businesses and individuals who use payment services through e-banking, with the number of transactions through this channel during the year 2011 reached 1,732,654 transactions, total transaction value of 49,436 billion VND (data from the State Bank of Viet Nam - Branch Ho Chi Minh City).

E-banking services also vary as (1) Internet banking for businesses and individual customers (transfers in and out of the system, the inter-bank transfer through CITAD...); (2) Mobile banking: SMS Banking (account balance inquiries, automatic SMS when there is a change in account...), Mobile banking (money transfer, bill payment ...); (3) Phone banking (payment of school fees, telephone payment,etc).

The commercial banks is cooperating with partners in the implementation of electronic payment services in order to reduce banking transaction costs, increased competition and improve the quality of services. This also helps reduce

the bank‟s cost in payments via papers, making it convenient for customers. Customers do not need go to the bank, but can make the payment transaction at

[ 10 ]

home. At the same time, the banks are also actively working with technology partners to provide security services, ensure the safety of customer and bank assets.

Table 1 – Figure of electronic banking services of 15 commercial banks

in Ho Chi Minh City (Report of the State Bank of Viet Nam - Branch Ho Chi Minh City on December 31, 2011)

Number of transactions Items (items) / Value (million

VND)

The number of enterprise customers using 7.561 payment via internet banking services

The number of individual customers are using 73.644

services paid via internet banking

The number of individual customers using 17.437 payment services via mobile banking

The number of customers using payment services 13.219 via other electronic banking

The number of payment transactions of business 325.846 customer via internet banking

Value of payment transactions of business 27.120.937 customer via internet banking

The number of payment transactions individual 1.056.905 customers via internet banking

Value of payment transactions individual 21.821.640

[ 11 ]

customers via internet banking

The number of payment transactions individual 334.700 customers via mobile banking

Value of payment transactions individual 494.543 customers via mobile banking

The number of payment transactions via other e15.203 banking

Value of payment transactions via other e31.570

banking

The above data shows that, individual customers using payment services over the internet are quite popular, much more than the number of users, the number and value of transactions compared to other electronic banking channels. Meanwhile, Vietnam has more people using mobile phone (mobile subscribers is 1.5 times of the population), but relatively low transaction through mobile banking.

1.2.2. Advantages of e-banking services

With payment services through electronic banking, customers can conduct banking transactions do not need go to the bank, not restricted by geographical area. They can sit at home to ordering, purchase a variety of goods and services quickly. Those services help customer saving time and costs. Therefore, in the short-term launch of electronic banking services, the number of customers using electronic banking service increased by favorable elements as follows:

The Government and the State Bank of Viet Nam promoted the commercial banks to develop electronic banking payment system.

The commercial banks has implemented core banking system based on modern technology, centralized data integration implemented

[ 12 ]

with partners in the co-payment, provide a convenient e-banking

payment method.

Electronic banking transaction is quick, convenient and low cost. The number of individual customers using the internet and online paymentvia the internet is increasing. In particular, the young customers can adaptable easily the new information technology. The commercial banks also save operating costs in counting and storage of cash. Today, more and more customers like to use ebanking payment services.

When e-commerce grows, many enterprises have focused investment to develop this type of business. So that, commercial bank have a function as a payment system. This conjunction will have two-way interaction, banking payment services via electronic banking and e-commerce will grow.

The commercial bank has an experienced team, invested infrastructure, specialized software and creat a variety of e-banking service, bring more benefits to customers.

Difficulties in implementing e-banking services

Vietnamese are afraid when dealing online. In particular, a number of Vietnam enterprises still consider with the payment service via e-banking, do not have the habit of e-banking transactions. Some customers concern about the safety of payment transactions through electronic banking channels.

Age factor, the older customer has limited to access new technologies and use of banking services on the computer as well as on mobile phone.

Social factor, the habit of using cash is a major obstacle.

[ 13 ]

Legal factor, whether electronic transaction law was effect, but crime in this area has not been discuss in detail.

The commercial banks need to invest in technology and infrastructure for effective implementation of e-banking services. Some banks have not invested properly, so infrastructure cannot meet the demand for payment transactions, sometimes happens the interrupt of customer transactions.

Problem statement

Mobile banking is popularly known is the recent trend in banking transition and holds a bright future that is promising over and above the one brought by ebanking. E-banking provided personalized, anytime - anywhere banking services. Nevertheless, with all the laudable benefits of mobile banking, it is yet to gain larger scale adoption, especially in the emerging economies.

According to Vietnam Bank Card Association, the development of personal accounts in Vietnam was very high, 124% average growth per year over the period 2006-2011. At June 30, 2011, the commercial banks have issued 36 million personal accounts, for 17 million people (about 20% of Vietnam's population). The promotion of mobile banking transaction channel enabled the banks to enhance their operations with cost cutting effectively and efficiently in order to handle daily banking affairs via mobile and Internet. Customer is convenient by reducing their visits to the banks and they can get their transactions via their mobile instead of personally visiting the branches. Although, the usage of mobile banking has been strong growth over the last few years, it is still in its infancy. According to the commercial banks survey of Nielsen Vietnam in August 2011, there are less than 3% banking customers using mobile banking services. This rate is very low if compared with the number of mobile subscribers nationwide at the end of December 2011. As the report of General Statistics

Vietnam, there was estimate at 117.6 million mobile subscribers, 32.6 million Internet users at the end of 2011. This figure is also

[ 14 ]

still very low when compare with developed nations in Asia. It means that mobile banking service in Viet Nam is a potential market in future.

During the last ten years, there was many studies concerning about the intention to use mobile banking. However, most of these studies focused on the West and the United States. In Asian region, most studies concentrated to developed Asian countries (Singapore, Hong Kong, Taiwan, Malaysia, ect…) than developing countries like Vietnam, Lao, Cambodia.

In Vietnam, mobile banking services are still in the initial stages of development. The commercial banks have a great deal for improvement. Thus, there is a need to study and understand users‟ acceptance of mobile banking services in order to identify the factors affecting their intention to use mobile banking. On the other hand, previous research for this topic in Vietnam is so limited. So that the topic was chose to study the factors affecting the adoption of mobile banking services, usefulness for the work and future research.

As a result, there is a necessity to research about the intention to use mobile banking in Ho Chi Minh City, Viet Nam. The finding of this study can help the marketers in the banking sector offer more suitable marketing strategies in their field in order to make higher attractiveness with mobile banking services.

1.4. Research objective

Specifically the objective of this study is to investigate and validate the factors influencing intention to use mobile banking.

The purpose of this study is to investigate and validate the factors influencing intention to use mobile banking in Ho Chi Minh City, Viet Nam. The study will

investigate these factors by using two different models: Technology Acceptance Model (TAM) and Theory Planned Behavior (TPB). The finding of this research may support some important information for leaders or marketers in doing their marketing strategies in banking industry. Marketers may base on these hypotheses to build suitable marketing plans for customers in Viet Nam.

[ 15 ]

1.5. Research scopes and limitations

This study is conduct in Ho Chi Minh City, one of the biggest economic centers of Viet Nam. The research object is the customers who are using the bank account, but have not yet registered to use mobile banking services. Due to limited time and cost, the research surveyed about 400 customers of two banks in HCMC (DongA Commercial Bank and Trust Commercial Bank). The result of research in this city, in some level can represent for Viet Nam in general and can be use as reference for further purposes.

The research will assess only in the intention to use mobile banking. The other factors such as the priority in choosing the bank, other services in banking… are out of the topic of this research. The other limitation relate to the sample size. The sample size of this study is 300 respondents only, somewhat limits the generalization of the research results. It would be reasonable to elevate sample size and testing this model more extensively, hence this future research would be more generalizable.

1.6. Research implications

This study has important implications for both practical business (leaders of commercial banks, marketing managers…) and academic (researchers, students of the business administration department…) as follow:

The research result has been a finding so that based on it, leaders of commercial banks will make an effective strategy to enhance customer satisfaction and improve operational efficiency. Marketing managers will consider the factors that affect customers‟ intention to use mobile banking to set up an appropriate communicate strategy.

The results of this study are a basis for researchers, students for developing further research to practical applications.

[ 16 ]

1.7. Thesis structure

This thesis organizes in five chapters. The first chapter is the introduction chapter. Furthermore, this chapter describes the overview of research backrground, research problem, and objective. Hence, the scope of research, implications, and structure of thesis are also present.

Chapter 2 is all about presenting previous research done on the stream of studies related to user‟s intention to use mobile banking. The chapter explains the history and development of Technology Acceptance Model and Theory of Planned Behaviour. And, how they are use in various contexts. This chapter is concentrates on explaining each variable in the model, and reasons for choosing them to be include in the research model.

Chapter 3 introduces research methodology and use to test the research model in previous session. It presents the research design, development of survey questionnaire, qualitative study, and main survey. The measurement scales apply for the research factors will be determined clearly and suitably.This chapter also defines how to collect data and analyse the data collected to test the research hypotheses proposed in chapter 2.

Chapter 4 translates data collected from survey, analyses data as well as discusses the result finding in connection with research model. This chapter explains the empirical part of the study. This part discusses the method for collecting data used to test the hypothesis, and it analyses the data received, its reliability and multiple regression.

The last chapter, chapter 5 discusses the results and research finding. This chapter concludes conclusion, implication, and research limitations. Finally, this thesis makes suggestions for further research on the topic area.

References and appendixes are included in the end of thesis.

[ 17 ]

1.8. Summary

The thesis begins the introduction by discussing the research background and importance of mobile banking in business and banking industry. Then the problem statement was defined to take a closed look at using mobile banking in Viet Nam. In order to narrow down the topic area, the scope of research examined the mobile banking in Ho Chi Minh City. Finally, this chapter discussed the research implications.

The most important thing to remember is that mobile banking provide vast amount of opportunities to the commercial banks and customers. This is a fact acknowledged all over the world, and not the least in Ho Chi Minh City where mobile usage in general are very popular.

It may seem that there is nothing to study in Ho Chi Minh City related to customer acceptance of mobile banking services, due to the fact this services is very useful for everybody. All the more reason, it is interesting and important to distinguish what are the factors that affect customer‟s intention to use mobile banking services.

[ 18 ]

CHAPTER 2: LITERATURE REVIEW

Before carrying out the survey on the effect factors of intention to use mobile banking, this chapter provide a theoretical background about Technology Acceptance Model, Theory of Planned Behaviour. Base on these, the conceptual research model and hypotheses are constructed.

1.1. Theoretical background

1.1.1. Technology Acceptance Model

There are several models existing that have been use to investigate adoption of technology. Several studies focusing on adoption of mobile services have their roots in Technology Acceptance Model (TAM) originally proposed by

Davies in 1986. The model is originally designed to predict user„s acceptance of

Information Technology and usage in an organizational context. TAM focuses on the attitude explanations of intention to use a specific technology or service; it has become a widely applied model for user acceptance and usage. TAM, shown in figure 1 was also the first model that established external variables as key factors in studying technology adoption.

Figure 1: Technology Acceptance Model

TAM model which deals with perceptions as opposed to real usage, suggests that when users are present with a new technology, two important factors influence their decision about how and when they will use it (Davis, 1989). These key factors are:

[ 19 ]

Perceived usefulness: This factor was defined by Davis as “the degree to which a person believes that using a particular system would enhance his or her job performance”.

Perceived ease of use: Davis defined this factor as “the degree to which a person believes that using a particular system would be free from effort”.

1.1.2. Theory of Planned Behavior

Theory of Planned Behaviour (Mathieson, 1991) has been proven successful in predicting and explaining human behavior across various information technologies. A person‟s actual behavior in performing certain action is directly influence by his or her behavioral intention. It was determine by attitude, subjective norm, and perceived behavioral control. Behavioral intention is a measure of the strength of one‟s willingness while performing certain behavior. Attitude (A) explains the feeling of a person‟s favorable or unfavorable assessment regarding the behavior in question. Furthermore, a favorable or unfavorable attitude is a direct influencing to the strength of behavior all beliefs about the likely salient consequences. Accordingly, attitude (A) is equated with attitudinal belief (Bi) linking the behavior to a certain outcome weighted by an evaluation of the desirability of that outcome (Ei) in question, i.e.A = ∑Bi*Ei. Subjective norm (SN) expresses the perceived organizational or social pressure of a person while intending to perform the behavior in question. In other word, subjective norm is relative to normative beliefs about the expectations of other persons.

It can be depicted as individual‟s normative belief (NBi) concerning a particular referent weighted by motivation to comply with that referent (MCi) in question, i.e.SN = ∑NBi*MCi. Perceived behavioral control (PBC) reflects a person‟s perception of ease or difficulty toward implementing the behavior in interest. It concerns the beliefs about presence of control factors that may facilitate or hinder

to perform the behavior. Thus, control beliefs about resources and opportunities are the underlying determinant of perceived behavioral control

[ 20 ]

and it can be depicted as control beliefs (CBi) weighted by perceived power of the control factor (PFi) in question, i.e. PBC= ∑CBi* PFi. In sum, TPB is propose to eliminate the limitations of the original model in dealing with the

Figure 2: Theory of Planned Behaviour

1.2. Research model and hypotheses

Based on the literature review, such as Technology Acceptance Model (TAM) and Theory of Planned Behaviour (TPB), this research proposes the research model indicated in Figure 3, including the five factors that have impact on intention to use mobible banking. These factors are perceived usefulness, perceived ease of use, attitude, subjective norm, and perceived behavioral control. There are also four controller variables (gender, marital status, age, education), using to analyze the influencing to the dependent variable.

[ 21 ]

Perceived usefulness

Perceived

H1

 Gender

ease of use H2

 Marital

Attitude H3 Intention status

to use

 Age

H4

Subjective

 Education

norm H5

Perceived behavioral control

Figure 3: The proposed research model with hypotheses

Perceived usefulness, and perceived ease of use

The elements perceived usefulness, perceived ease of use and attitude toward adoption of mobile banking were adapted from TAM (Davies, 1986). These elements have also been maintained for studying the adoption of mobile banking services where results fairly well comply with the findings from TAM studies. Perceived usefulness refers to the degree to which an individual believes that the usage of technology will enhance his or her performance (Davis, 1989). Previous

research findings have shown the positive influence of perceived usefulness toward the adoption and usage of an information system (Davis, 1989; Luarn & Lin, 2005).

Perceived ease of use refers to the degree to which one believes that using an information system is free from effort (Davis, 1989). Previous research works have found that “Perceived ease of use” has positive influenced on intention to use of technology (Davis, 1989; Luarn & Lin, 2005). With research on the customers‟s intention to use mobile banking in China, Luarn & Lin (2005) found

[ 22 ]

that “Perceived ease of use” was significant in determining intention to use mobile banking. Individuals will adopt and use mobile banking services if they perceive it as ease to learn and use.

Thus, they will affect user‟s attitude. Hence, the following hypotheses are proposed:

H1: Perceived usefulness positively affects intention to use mobile banking.

H2: Perceived ease of use positively affects intention to use mobile banking.

Attitude

Attitude toward behavior “refers to the degree to which a person has a favourable or unfavourable evaluation or appraisal of the behavior in question”

(Ajzen, 1991, p.188). Attitude was populated to be the first antecedent of behavioral intention. It is an individual‟s positive or negative belief about performing a specific behavior. An individual will intend to perform a certain behavior when he or she evaluates it positively. It has been demonstrate that attitude has a strong effect, direct and positive, on the real individual intentions to use a new system or technology (Bobbitt & Dabholkar, 2001; Dishaw & Strong, 1999). In this research, attitude is hypothesized to influences the intention toward using mobile banking services, and is defined as the degree to which an individual‟s attitude is favourably or unfavourably disposed toward using mobile banking services. The impact of attitude on intention to use mobile banking is capture in the next hypothesis.

H3: There are significant positive relationship between attitude and intention to use mobile banking.

Subjective norm

This construct was promoted by Fishbein & Ajzen (1975), and was developed by Mathieson (1991). Subjective norm refers to the perceived social

[ 23 ]

pressure to perform a behavior; according to what others say or do is important (Mathieson, 1991, p.34). Accordingly, subjective norm is a social force and relates to the normative beliefs about the expectation from relevant persons. In this research, subjective norm is define as customers consider the normative expectations of others they view as important, such as family, friends, and colleague, to decide if whether they use mobile banking services. Previous studies have explored the importance of such construct in social science studies including in banking studies by Nysveen (2005) who examined mobile banking usage in Norway, and found the subjective norm is an important driver for mobile chatting usage among the Norwegians. Thus, the following hypothesis is proposed:

H4: Subjective norm positively affects intention to use mobile banking.

Perceived behavioral control

Perceived behavioural control refers to the constraints to technology usage (Taylor & Todd, 1995). It refers to people's perceptions of their ability to perform a given behavior. Drawing an analogy to the expectancy-value model of attitude, it is assumed that perceived behavioral control is determined by the total set of accessible control beliefs, i.e., beliefs about the presence of factors that may facilitate or impede performance of the behavior. Specifically, the strength of each control belief (c) is weighted by the perceived power (p) of the control factor, and the products are aggregated, as shown in the following equation. To the extent that it is an accurate reflection of actual behavioral control, perceived behavioral control can, together with intention, be used to predict behavior.

Thus, the following hypothesis is proposed:

H5: Perceived behavioral control positively affects intention to use mobile banking.

[ 24 ]

1.3. Summary

Technology Acceptance Model and Theory of Planned Behaviour are chose as a basis for this study. The reason for choosing they are, that the models have been successfully used in several previous researches related to retail bank customers. Additionally, similar determinants can be acknowledge to influence the user acceptance and adoption of mobile banking, whether or not these studies have been using Technology Acceptance Model and Theory of Planned Behaviour as the framework.

The following five items have been identified as common determinants of predicting the intention to use mobile banking. Therefore, the researcher selected for closer investigation in this research:

Perceived Usefulness: “The degree to which a person believes that using a particular system would enhance his or hers job performance”

(Davis, 1989).

Perceived Ease of Use: “The degree to which a person believes that using a particular system would be free from effort” (Davis, 1989).

Attitude: “refers to the degree to which a person has a favourable or unfavourable evaluation or appraisal of the behavior in question”

(Ajzen, 1991).

Subjective norm: “refers to the perceived social pressure to perform a behavior; according to what others say or do is important” (Mathieson, 1991).

Perceived behavioural control: “refers to the constraints to technology usage” (Taylor & Todd, 1995).

The reviewed literature works as a good basis in developing a research model to measure the factors that influence customers‟ intention to use mobile banking and their decision on whether or not to use the bank‟s mobile banking services.

[ 25 ]

CHAPTER 3: METHODOLOGY

This chapter introduces research methodology and use to test the research model in previous session. It presents the research design, development of survey questionnaire, qualitative study, and main survey. This chapter also defines how to collect data and analyse the data collected to test the research hypotheses proposed in chapter 2.

3.1. Research design

This study used two research methods. The first phase, qualitative research identifies the models, factors, suitable measurement variables for research in HCMC. Through the previous relevant researches, the questionnaire was built then running the pilot test for checking the efficiency and the meaning of the questions. The pilot test was purposed to explore and define the relevant items and building a completed questionnaire. The second phase, quantitative survey was the main approach of this study. The goal is to identify the factors affecting customers‟ intention to use mobile banking services.

Research process includes the steps as illustrated in Figure 4:

Problem

Literature

The draft of

definition

review

questionaire

The final

Revised

Pilot test

questionaire

Quatitative

Cronbach

Exploratory

research

alpha

factor analysis

Multiple

Completed

regression

measurement

analysis

scale

Figure 4: Research process

[ 26 ]

3.2. Development of questionnaire

3.2.1. Measurement scales

The measurement scales used in this study wa multi-item five point Likert scales, which developed and validated by previous researches (Davis et al, 1989); Igbaria et al, 1997; Fishbein & Ajzen, 1975; Triandis, 1977; Bagozzi, 1984; Taylor & Todd, 1995; Chua, 1980 and Ajzen, 1991).

3.2.1.1. Measure of Perceived usefulness and Perceived ease of use

Perceived usefulness and ease of use are important technology adoption determinants in the technology acceptance model (Davis, 1989). Perceived usefulness is defined as the extent to which a person believes that using a particular technology will enhance her or his job performance, while Perceived ease of use is the degree to which using IT is free of effort for the user (Davis, 1989). A significant body of previous study has shown that perceived usefulness and perceived ease of use are determinants of intention to use mobile services (Igbaria et al., 1997).

Eight observed variables with a five-point Likert scales from Davis et al

(1989) and Igbaria et al (1997) were primarily used to measure customer‟s intention to use mobile banking.

Table 2: Scale of Perceived usefulness and Perceived ease of use

Construct Coding of

Items

variables

PU01 Flexibility to conduct banking business 24 hours per day

Perceived PU02 Make banking transactions quickly

usefulness

PU03 Help me using MB easier than banking teller

PU04 MB transactions relevant to my work

[ 27 ]

PU05 I feel very comfortable when using MB

PU06 Learning to use MB is easy for me

PEU07 Instructions in MB system are clear and understandable

Perceived

PEU08 MB has many flexible ways to search your required

ease of use

information

PEU09 I feel that user-friendliness of MB services is important

3.2.1.2. Measure of Attitude

Attitude was defined as an individual‟s positive or negative feeling

(evaluative effect) about performing the target behaviour (Fishbein & Ajzen, 1975). It is related to behavioural intention because people form intentions to perform behaviours toward which they have positive feeling. The attitude - behavioural relationship is fundamental to TAM and related models presented by other researchers such as, Triandis (1977) and Bagozzi (1984). With regards to mobile banking, intention to use mobile banking was positively influenced by attitude towards this model.

Attitude was measured on three observed variables, a five-point Likert scale developed by Fishbein & Ajzen (1975), Triandis (1977) and Bagozzi (1984).

Table 3: Scale of Attitude

Construct Coding of variables Items

ATT10 Using MB would be a wise idea

Attitude ATT11 Using MB is a good idea

ATT12 I like to use MB

[ 28 ]

3.2.1.3. Measure of Subjective norms

Subjective norms refer to “the person‟s perception that most people who are important to him think he should or should not perform the behaviour in question” (Fishbein & Ajzen, 1975, p.302). Subjective norms have been found to be more important prior to, or in the early stages of innovation implementation when users have limited direct experience from which to develop attitudes (Taylor & Todd, 1995). Chua (1980) suggests that the adopter‟s friends, family, and colleagues are groups that will potentially influence adoption.

Six observed variables with a five-point Likert scale from Taylor & Todd (1995) and Chua (1980) were used to measure subjective norms.

Table 4: Scale of Subjective norms

Construc Coding of Items

t variables

SN13 People important to me would think that using MB would

be a wise idea

SN14 People important to me would think that using MB is a

good idea

SN15 Most people important to me would think that I should use

Subjectiv

MB

e norms

SN16 My family important to me would think that using MB

would be a wise idea

SN17 My family important to me would think that using MB is a

good idea

SN18 My family important to me would think that I should use

MB

[ 29 ]

3.2.1.4. Measure of Perceived Behavioural Control

Perceived Behavioural Control is defined as “…people‟s perception of the ease or difficulty of performing the behavior of interest” (Ajzen, 1991, p.183).

Consistent with this basic definition, previous research studying technology adoption and usage behavior has related this construct primarily to constraint to technology usage (Taylor & Todd, 1995), particularly the ease or difficulty of using the new technology.

Perceived Behavioural Control was measured on three items, developed by Ajzen (1991) and Taylor & Todd (1995).

Table 5: Scale of Perceived Behavioural Control

Construct Coding of variables Items

PBC19 I would be able to operate MB

Perceived

Behavioural PBC20 I have the resource to use MB

Control

PBC21 I have the knowledge to use MB

3.2.1.5. Measure of Intention to use mobile banking

Intention to use mobile banking was measured by three observed variables, developed by Ajzen (1991), used a five-point Likert scale, and modified by the author as follow:

Table 6: Scale of Intention to use mobile banking

Construct Coding of Items

variables

INT23 Plan to use MB

Intention to use INT24 Intention to use it within the next tree

mobile banking

months

INT25 Add MB to my favorite apps

[ 30 ]

In summary, based on the previous research and after refinement, 25 variables are select and group in five indenpent variables and a dependent variable.

3.2.2. Draft questionnaire

The questionnaire using a five-point Likert scale was employe to collect the data for the factors of the research model. Items selects for the factors are mostly adapted from previous studies in order to ensure content validity. Items measuring perceived usefulness, ease of use, items of attitude, subjective norm, perceived behavioral control, and intention to use was take from previous research (see in 3.2.1. Measurement scales).

Structure of questionnaire consists of three main parts:

Part 1: General information to get information about the respondent‟s banking transactions (open accounts, mobile banking services). This information helps select the target respondent to study.

Part 2: The main information includes statements (questions) are based on a scale of measurement was proposed for the research. The items were measured on the Likert 5-point scale from 1 to 5 (strongly disagree, disagree, neutral, agree, and strongly agree).

Part 3: Questions on demographics characteristics are included at the end of the questionnaire.

The survey questions were translate from English to Vietnamese by the researcher, and edited by other.

3.3. Pilot study

Due to the scale of research are adopt from the scales of the previous researches. These researches were conduct in different culture, the level of economic development and selected respondents. Therefore, a pilot study was conduct through qualitative research method. The purpose is to gather

[ 31 ]

information, and adjust variables in these scales. The wording Vietnamese language for these scales is also doing to study. So that, respondents can understand the question, to avoid confusion.

To conduct qualitative research, the pilot test is conduct in Ho Chi Minh City. The 10 questionaires (translated to Vietnamese language) were sent to 4 banking experts and 6 customers for answering. After five days, the forms have been return for the pilot test. Some small changes on the questionnaire fits with understand of respondents and make clear for the questions (in Vietnamsese language).

3.4. Sample method

The target population is the banking customers who are using individual account, but not signed up for mobile banking services. Four hunreds customers were sampled conviently from a database of DongA Commercial Bank and Trust Commercial Bank (selected 200 customers / bank).

According to Hair and Anderson (1998), a general rule, the sample size should be 100 or greater. For standard multiple regression analysis, Tabachnick and Fidell (1996) proposed that the desired level is: n > 50 + 8m (where m= number of independent variables). Hence, the required sample is: n > 50 + 8*5 = 90. Thus, the minimum sample size is 100.

The sample size is determined according to technique of multi variable analysis. Factor analysis and multi regression method are used for this research. In factor analysis, the sample size should be as large as possible with the minimum should be at least five times as many observations as the number of factors to be analyzed and preferably not less than 100. As there are 25 variables used for the factors analysis, the minimum sample size should be 100 (20x5). In addition, for

the multi regression method, minimum sample size should be equal to n=50+15m, which m are number of independent variables (Tabachnick and Fidell, 1996). With the initial research model, there are five independent

[ 32 ]

variables; minimum sample size for this should be 125 (50+15*5). In light of the above two requirements, this research choose the biggest sample size. Therefore, minimum sample size for this research should be 123.

3.5. Questionnaire administration

The respondents are filtered from the data of the above two banks will submit questions through the online survey. After a week, there are 128 customers answered the questionnaire online survey. The researcher has call to confirm and invite other customers to participate in this survey. A week later, 120 customers participated in the survey.

The quantitative survey via online was conducted in HCMC on October

2012.

3.6. Data analysis methods

This section was important in assuring the reliability of the constructs and thus controlling data generated through questionnaires.

3.6.1. Reliability

According to George and Malley (2003), “Cronbach‟s alpha is used as only one criterion for judging instruments or scales. It only indicates if the items

“hang together”; it does not determine if they are measuring attribute. Therefore, scales also should be judged on their content and construct validity”. George and Malley (2003, cited in Matkar, p.94) provide the following techniques:

Table 7: Cronbach’s alpha reliability coefficient

Cronbach’s alpha Internal consistency α ≥ 0.9 Excellent 0.8 ≤ α < 0.9 Good 0.7 ≤ α < 0.8 Acceptable 0.6 ≤ α < 0.7 Questionable 0.5 ≤ α < 0.6 Poor α < 0.5 Unacceptable

[ 33 ]

3.6.2. Exploratory Factor Analysis (EFA)

Norris and Lecavalier (2010, p.9) supposed that “EFA is based upon a testable model and can be evaluated in terms of its fit to the hypothesized population model; fit indices can be generated to help with model interpretation”. And “EFA‟s purpose is to identify latent constructs underlying a set of manifest variables”. Hair et al. (1998), Lee & Hooley (2005, p.376) claimed that with samples of 300 or more, a factor loading of the attribute higher than 0.3 is significant. And with samples of 200, a factor loading of 0.4 or greater will take to indicate. Therefore, the researchers must carefully consider the sample size for choosing significant factor loadings. Moreover, factors with a total eigenvalue of 1 or greater will take into account; hence any factors with an eigenvalue of less than 1 are discounted (Lee & Hooley, 2005, p.376). Based on these studies, any factors with eigenvalues greater than 1 would be retained. And any factor loadings of 0.3 or higher on a factor are counted.

3.6.3. Multiple regression analysis

Meyers, Gamst, and Guarino (2006, p.152) and Hair et al. (2010, p.156) proposed that the multiple regression standardized score equation is as follows (with all the variables are measured on the same metric):

Y = β1X1 + β2X2 + … + βnXn

Where in: β is called beta weight, standardized regression coefficient, or beta coefficient

X is the predictor entered into the equation in a single step

βX represents the score of a predictor and its associated beta weight

In addition, Hair et al. (2010) claimed that there is the difference between the actual and predicted values of dependent variable. That means the random error will occur when predicting sample data. It is call the residual (ε or e).

Based on these studies, the multiple regression formula will be

[ 34 ]

Y = a + β1X1 + β2X2 + … + βnXn + ε

Moreover, Meyers et al. (2006, p.161) introduced the value of R2 indicating how much variance of the dependent variable is accounted for by the full regression model. Therefore, the higher the value of R2, the greater the explanatory power of the regression equation (Hair et al., 2010).

3.7. Summary

After collected data was be cleaned, remove the invalid questionnaires and data will be processed using software SPSS 16.0 (Statistical Package for Social Sciences). Collected data was analyzed and interpreted in a series of stage. First, the demographic profile of respondents was summarized and analyze. Second, the reliability of the items used in measuring the constructs was validated using

Cronbach‟s alpha. Third, the correlation of the independent variables and the dependent variable was ascertained through Promax method. At last, standard multiple regression analysis was used to establish the statistical significance of the model and the predictive power of each independent variables in explaining the dependent variable (intention to use mobile banking).

[ 35 ]

CHAPTER 4: DATA ANALYSIS AND RESULTS

The purpose of this chapter was to present finding which were collected from the actual questionnaire survey. Beside, the researcher proposed an official assessment of measures and also carried out the analysis to give the accurate answers for the research questions, hypotheses proposing in the chapter 2.

4.1. Descriptive analysis

A total of 400 people were sampled, 248 responses were received and 225 questionnaires were useable for analysis (equal 56.3%). The data was gathered on personal banking customers of many commercial banks in Ho Chi Minh City, but they do not use mobile banking services yet. The gender distribution of the survey respondents is 56.4% for males and 43.6% for females.

The results also indicated that the research samples had age predominantly between 21 and 30 years, which accounted for 59.6%. Regarding marital status, the single respondents accounted for more than 60.4%, while married respondents were 39.6%. With the education factor, the highest percentage is the graduate group that accounted for 68%. Table 8 gives a detailed description of the demographic statistics for the respondents.

Table 8: Descriptive statistic of respondent’s characteristics Measure Value Frequency Percent

Male 127 56.4

Gender Female 98 43.6

Total 225 100.0

Single 136 60.4

Status Married 89 39.6

Total 225 100.0

20 or lower 19

8.4

21-30 134 59.6

Age 31-40 51 22.7

41-50

17 7.6

51 or higher 4 1.8

[ 36 ]

Measure

Value

Frequency

Percent

Gender

Male 127

56.4

Female 98 43.6

Total 225 100.0

Bachelors

40

17.8

Graduate

153

68.0

Education

Postgraduate

10.7

24

Others

3.6

8

Total

100.0

225

4.2. Measure assessment 4.2.1. Reliability analysis

To ascertain the reliability of the measurement scales and to check the degree to which the items that make up the scale “hang together”, Cronbach alpha coefficient is calculated. Cronbach‟s alpha checks the internal consistency reliability of scales. It checks if whether the items that make up the scale actually measure the same underlying construct (Pallant, 2001). For scale to be reliable, its Cronbach alpha value should be above 0.6 (George & Mallery, 2003).

The above guideline indicates that the higher the Cronbach‟s alpha value is, the more reliable are the items measuring a give construct. Cronbach‟s alpha closer to 1.0 is preferred. A Cronbach‟s alpha value of 0.9 and above was regarded as the most reliable of scales, while a scale that has a Cronbach ‟s alpha value that is below 0.5 is regarded as unreliable and cannot be used to measure a given construct.

[ 37 ]

Table 9: Reliability analysis for each factor

Corrected Item-

Scale Mean if Scale Variance if Total Cronbach's Alpha

Item Deleted Item Deleted Correlation if Item Deleted

PU01 17.07

2.941 .458 .533 PU02 17.34 2.726 .603 .459 PU03 17.78 3.433 .207 .652 PU04 17.12 3.067 .291 .622 PU05 17.70 3.051 .382 .570

Perceived Usefulness Cronbach's Alpha .625

PEU06 10.95 4.069 .569 .886

PEU07 11.52 3.471 .779 .804

PEU08 11.24

3.769 .792 .805

PEU09 11.46 3.353 .753 .817

Perceived ease of use Cronbach's Alpha .867

ATT10 7.57 2.255 .740 .871

ATT11 7.08 1.838 .798 .823

ATT12 7.32 2.012 .804 .813

Attitude Cronbach's Alpha .885

SN13 16.63 7.502 .701 .807

SN14 16.12 7.574 .824 .780

SN15 15.80 8.310 .573 .833

SN16 16.67 9.554 .473 .847

SN17 16.40 8.678 .715 .810

SN18 16.04 8.578

.530 .841

Subjective norm Cronbach's Alpha .846

PBC19 11.09 3.188 .692 .808

PBC20 10.37 3.261 .582 .849

PBC21 11.50 2.644 .769 .770

PBC22 10.95 2.770 .720 .793

Perceive behavioral Cronbach's Alpha .848

control

INT23 6.90 2.124 .771 .887

INT24 7.19 1.995 .856 .811

INT25 7.39 2.248 .787 .872

Intention to use MB Cronbach's Alpha .901

[ 38 ]

Table 8 depicts a summary of the beta scores of all the response ranking of the factors that affect the intention to use mobile banking in HCM. According to the data analysis, the factor "Perceived Usefulness" Cronbach's alpha coefficient is 0.625. After delete PU03 item of this factor, then the factor "Perceived Usefulness" Cronbach's alpha coefficient increased to 0.652. Other factors exhibit a Cronbach‟s alpha coefficient from 0.846 to 0.901. Among the factors, the factor “intention to use mobile banking” has the highest ranking of Cronbach alpha of 0.901, followed by the factor “perceived ease of use” and “attitude” with 0.867. The factor “subjective norm” has the lowest ranking with 0.846. Hence, six variables were retained.

4.2.2. Exploratory Factor Analysis (EFA)

The test of the measurement model includes the estimation of using item-to-total correlations (>0.5) and the convergent and discriminant validity of the instrument items. The convergent validity is demonstrated when items load highly (loading> 0.5) on their associated factors.

Dependent variable and the independent variables were analyzed independently of each factor. All the variables were extracted through the Principal axis factoring, using the Promax rotation method. After three times of running factor analysis, the result shows that all remained variables are greater than 0.5 and a significant loading on an acceptable factor. Advanced data processing steps as follows:

The first time running factor analysis for independent variables, there are six factors with the eigenvalues that are higher than 1 (see Appendix 5). The variables PU05, PEU06, PBC20 have a cross-loading (see Appendix 6). Therefore, they were deleted from the analysis and the loadings recalculated.

The second time running, six factors were extracted (see Appendix 8).

The last time, running factor analysis for dependent variables.

[ 39 ]

Rotation was converged in six iterations. The generated factors, the attributes of each factor loadings are present in Table 9. All items have large and significant loadings on their corresponding factors. The composite reliabilities of the different measures included in the model ranged from 0.645 to 0.955. Further, the shared variance is less than the amount of variance extracted by the indicators measuring the constructs. Thus, the convergent and discriminant validity are meet. Taken together, the evidence indicates the scales' had adequate psychometric quality for usage in the next stage of analysis.

After processing factor analysis for the independent variables and the dependent variable by the Promax method has five factor are formed, there are three items (observed variables) are removed because values less than 0.5. The 22 items were selected and have values above 0.5, with significant dependent variable. The factors form after the implementation of EFA: perceived usefulness, perceived ease of use, attitude, subjective norm, perceived behavioral control, and intention to use mobile banking.

[ 40 ]

Table 10: Key dimensions, items Dimension Factor Loading

PU01 - Flexibility to conduct banking business 24 hours per .742

X1 day

Perceived

PU02 - Make banking transactions quickly .648

usefulness

PU04 - MB transactions relevant to my work .497

PEU07 - Instructions in MB system are clear and .729

understandable

X2 -

PEU08 - MB has many flexible ways to search your required .801

Perceived

information

ease of use

PEU09 - I feel that user-friendliness of MB services is .979

important

ATT10 - Using MB would be a wise idea .779

X3 -

ATT11 - Using MB is a good idea .894

Attitude

ATT12 - I like to use MB .921

SN13 - People important to me would think that using MB

.688

would be a wise idea

SN14 - People important to me would think that using MB is a .653

good idea

X4 – SN15 - Most people important to me would think that I should .957

use MB

Subjective

SN16 - My family important to me would think that using MB .860

norm would be a wise idea

SN17 - My family important to me would think that using MB .902

is a good idea

SN18 - My family important to me would think that I should .783

use MB

X5 PBC19 - I would be able to operate MB .958

Perceived

PBC21 - I have the knowledge to use MB .752

behavioral

PBC22 - I have the ability to use MB

.621

control

Y– INT23 - Plan to use MB .815

Intention

INT24 - Intention to use it within the next tree months .948

to use

INT25 - Add MB to my favorite apps

mobile

.841

banking

[ 41 ]

4.3. Hypotheses testing

To test the hypotheses advanced, multiple regression analysis was use. The multiple regression analysis is an advanced extension of correlation, where one variable (the dependent variable) can be predict based on a number of variables (independent variable). Multiple regression analysis aids in testing model and hypotheses. It gives us information about the model as a whole and the relative significance (contribution) of each factor that form the model. It is a most suitable statistics when you have a set of continuous independent variables (two or more) and one dependent variable (Pallant, 2001). Two key statistical analysis 2

are important here; the squared multiple correlation coefficient (R ), and the 2

standardized coefficient weight (beta weight). The R shows how much of variance in the dependent variable (in my case intention to use mobile banking) is explained by the model (preditor variables). The beta value on the other hand, tells us the significance of each independent variable (in the model) in predicting 2

the dependent variable (intention to use mobile banking). Both the R and beta value range between 0 and 1.0, the more closely the vlue is to 1.0 the better. Below (Table 11) is the summary of multiple regression analysis conducted.

2

Table 11: R Square Value (R )

Model Summary Model R R Square Adjusted R Std. Error of

b

Square the Estimate

1 a

.859

.819 .818 1.08361

Predictors: (Constant), X1, X2, X3, X4, X5

Dependent Variable: Intention to use mobile banking

Table 11 above (model summary) shows the value of R Square and the Adjusted 2

R Square value. The R value tells us the amount of variance in the dependent variable (intention to use mobile banking) accounted by the model. A high variance indicates a high level of success of the model. Sometimes, the R square value have a propensity to somewhat overrate the success of the model

[ 42 ]

when applied to the real world scenario. The Adjusted R Square value provides a more correct estimate measure of the success of the model. In my own case, the R square value 0.819 and the Adjusted R Square is 0.818. This implies that the model accounts for 81.9% (expressed as percentage) of the variance in intention to use mobile banking. To better depict a true estimate the Adjusted R Square indicates that the model explains 81.9% of the variance in the dependent variable. This is very good model when compared to findings reported in past journal articles (Chau & Hu, 2001).

b

Table 12: ANOVA

Mean

Model Sum of Squares df

Square F Sig.

1 Regression 2931.071 5

586.214

.000

a

499.243

Residual 257.151 219

1.174

Total 3188.222 224

Predictors: (Constant), X5, X3, X1, X4, X2

Dependent Variable: Intention to use mobile banking

The next table (Table 12) shows the ANOVA which evaluated the statistical significance of the model. The result shows that the research model is significant (Sig. = .000, meaning that p < .005).

Table 13: Coefficients

Standardize

Unstandardized d

a

Collinearity

Coefficients Coefficients

Statistics

Model B Std. Error Beta t Sig. Tolerance VIF

1 (Constant) -3.432 .817

-4.201 .000

X1 .324 .052 .231 6.273 .000 .847 1.180

X2

.061 .042 .133 1.447 .049 .717 1.396

X3 -.016 .037 -.009 -.435 .664 .896 1.116

X4 .031 .025 .128 1.254 .031 .731 1.369

X5

.832 .052 .577 15.345 .000 .598 1.672

a. Dependent Variable: Intention to use mobile banking

[ 43 ]

The last table (Table 13) presents the coefficient Sig. and Standardized Beta Coefficient. This value tells us the unique contribution of each independent variable to the model when other predictor variables are controll. A large value implies that the underlying variable made a significant contribution to the model. Looking at the Standardized Beta column, we can see that only four varibles: X1 - Perceived ease of use (Beta = .231), X2 – Perceived ease of use (Beta = .133), X4 – Subjective norm (Beta = .128), and X5 – Perceived behavioral control (Beta = .577) made significant contribution to the model. Overall, perceived behavioral control made the largest contribution in explaining the dependent variable. The t and Sig (p) values indicate the statistical significance of each independent variable in predicting the dependent variable. A large absolute t value and a small p value (p < .05) points out that a predictor variable is significant in predicting the dependent variable. From the result of the analysis (Table 4.6) only perceived usefulness (t = 6.273 and p = .000), perceived ease of use (t = 1.447 and p =

.049), subjective norm (t = 1.254 and p = .031), and perceived behavioral control (t = 15.345 and p = .000) are significant factors in predicting intention to use mobile banking.

From the results obtained in the multiple regression analysis, only perceived usefulness, perceived ease of use, subjective norm, and perceived behavioral control are significant and they explain 81.8% of the variance in the dependent variable (intention to use mobile banking). Thus, hypotheses H1, H2, H4 and H5 are accepted. On the other hand, hypothesis H3 are not support and thus rejected in this study. Perceived usefulness, perceived ease of use, subjective norm, and perceived behavioral control were found to be insignificant in explaining the variation in the dependent variable.

Formula: Y = 0.231X1 + + 0.133X2 + 0.128X4 + 0.577X5 + ɛ

Y – Intention to use mobile banking; X1 - Perceived usefulness; X2 - Perceive easy of use; X4 – Subjective norm; X5 - Perceived behavioral control

[ 44 ]

4.4. Testing the effect of demographic variables

Table 14: Creating and recoding variables

No. Variable Label Group description Recoding

1 C1 Gender Male vs. female MAL (male

group)

Younger (20 or less), 21YOU

2 C2 Age 30, 31-40, 41-50 vs. older

(younger group)

(over 50)

3 C3 Status Single v.s married SIN

(single group)

Bachelors, Graduate, BAC

4

C4 Education Postgraduate, PhD vs. (Bachelors

others group)

Results of multi regression analysis (see Appendix XIII) are as follows:

Gender: the results show that difference was found in terms of banking customer gender with the value of .049 (