ER009_Factors Affecting the Adoption of Internet Banking

Factors Affecting the Adoption of Internet Banking in South Africa : a Comparative Study

ER Project 2001 – Hoppe, Mugera, Newman 1

Factors Affecting the Adoption of Internet Banking in South Africa :

a Comparative Study

An Empirical Research Paper presented to the

Department of Information Systems

University of Cape Town

in partial fulfillment of the requirements for the course on

Information Systems Honours (INF 414 W)

By

Rudi Hoppe

Paul Newman

Pauline Mugera

17 October 2001



PREFACE

This report and the conclusions made herein are not confidential, however participant

data is confidential and every effort has been made to ensure this.

The authors would like to thank:

• Mr Irwin Brown for his assistance and guidance throughout the duration of the

project.

• The respondents to our survey

• Margaret Tan and Thompson S.H. Teo for allowing permission to use their

questionnaire.

The authors certify that this report is their own work, and that all references are

accurately reported

_________________ _____________________ _________________

Rudi Hoppe Pauline Mugera Paul Newman



ABSTRACT

A research framework based on the theory of planned behaviour and the diffusion of

innovations theory developed by Tan & Teo (2000) was used to identify the

attitudinal, social and perceived behavioural control factors that might influence the

adoption of Internet banking. The set of questions developed by Tan & Teo (2000) for

their Singapore study was then used in an online questionnaire for SA Internet users

with the aim of obtaining results which could be compared with the previous results.

The results were largely in agreement with those obtained in the Singapore study. It

was confirmed that attitudinal and perceived behavioural control factors, rather than

social influence play a significant role in influencing the intention to adopt Internet

Banking services. In particular, perceptions of relative advantage, compatibility,

trialability and risk regarding Internet use were found to influence the intention to

adopt these services. There were 3 notable differences in the results. Firstly,

Complexity was not found to be a significant factor in Singapore, whereas it was in

this study. In addition to this, Government support and banking needs were found to

be factors in Singapore, but neither was found to be significant from the SA results.

The implications of the study are discussed and various conclusions are reached.

Keywords : Internet Banking, Adoption, South Africa



TABLE OF CONTENTS

PREFACE.................................................................................................................................................................2

ABSTRACT .............................................................................................................................................................3

TABLE OF CONTENTS......................................................................................................................................4

1. INTRODUCTION..............................................................................................................................................6

2. DEFINITIONS....................................................................................................................................................7

2.1 ADVANTAGES OF INTERNET BANKING........................................................................................................8 2.2 DISADVANTAGES OF INTERNET BANKING.................................................................................................9

3. INTERNET BANKING IN SOUTH AFRICA..........................................................................................9

4. RESEARCH FRAMEWORK....................................................................................................................... 11

4.1 VARIABLES AND HYPOTHESIS ................................................................................................................... 12

5. RESEARCH DESIGN .................................................................................................................................... 18

5.1 THE QUESTIONNAIRE........................................................................................................................... 19 5.2 HOSTING.................................................................................................................................................... 21 5.3 SPONSORSHIP .......................................................................................................................................... 21 5.4 ADVERTISING.......................................................................................................................................... 22 5.5 RESPONSE RATE..................................................................................................................................... 22 5.6 RELIABILITY AND VALIDITY ASSESSMENT.............................................................................. 23

6. FINDINGS AND ANALYSIS ....................................................................................................................... 24

6.1 PREFERRED INTERNET SERVICES AND PRODUCTS................................................................ 27

7. LIMITATIONS OF TH IS STUDY ............................................................................................................. 31

8. DISCUSSIONS AND CONCLUSIONS............................................................................................... 32

9. BIBLIOGRAPHY ............................................................................................................................................ 34

APPENDIX A – FACTOR ANALYSIS.......................................................................................................... 36

TABLE A2. VIF VALUES .................................................................................................................................38

LIST OF TABLES TABLE 2.1 FEATURES AND FUNCTIONS ...............................................................................................................7

TABLE 3.1: INTERNET BANKING RATES ........................................................................................................... 10

TABLE 4.2 T ABLE OF HYPOTHESISES................................................................................................................ 13

TABLE 5.1. ATTITUDE VARIABLES..................................................................................................................... 19

TABLE 5.2. PERCEIVED BEHAVIOURAL CONTROL VARIABLES..................................................................... 20

TABLE 5.3 RELIABILITY ANALYSIS USING CRONBACH ’S ALPHA................................................................ 23



TABLE 6.1 DEMOGRAPHIC PROFILE OF RESPONDENTS ................................................................................ 24

TABLE 6.2 RESULTS OF LINEAR REGRESSION.................................................................................................25

TABLE 6.3 INTERNET BANKING S ERVICES ....................................................................................................... 28

TABLE 6.4 T AN & TEO’S RESULTS - INTERNET BANKING SERVICES.......................................................... 28

TABLE 7.1 CRITERIA FOR S ELECTING AN ONLINE BANKING S ERVICE ..................................................... 29

TABLE 7.2 T AN & TEO’S RESULTS - CRITERIA FOR SELECTING AN O NLINE B ANK S ERVICE............... 29

TABLE 7.3 PREFERRED INTERNET CHARGES ..................................................................................................30

TABLE A1. FACTOR ANALYSIS............................................................................................................................ 37

TABLE A2. VIF VALUES ....................................................................................................................................... 38



1. INTRODUCTION

In a world, which is becoming increasingly globalised through the use of the Internet

and the World Wide Web (WWW), Internet banking has been gaining ground as a

new opportunity for banking institutions. These new opportunities and challenges,

have meant the rise of new competitors in the global banking market [Suganthil,

Balachander and Balachandran 2001],

Banking is a highly information-intensive industry. Customers demand accurate

information regarding their accounts and this information needs to be easily

accessible. As a result information technology is extensively used in the collection,

processing and output of information to users and customers.

Most banks use the Internet as a distribution channel through which financial services

may be offered at lower costs to a wider spectrum of potential customers. This allows

for contacts from all over the world to be established at any one time. Consequently

this allows banks the opportunity to expand their operations beyond just their

geographical locations.

The objective of this paper is to identify and describe the factors influencing the

adoption of Internet banking in South Africa and compare them with those identified

in Singapore. The paper is organised as follows: firstly it provides a definition of

Internet banking as well as its advantages and disadvantages, followed by an

overview of Internet banking in South Africa. The research framework and research

design, utilised in the study, is outlined prior to a discussion of the results obtained.

The paper concludes by analysing the research implications of the findings.



2. DEFINITIONS

Online banking is a term used to encompass all banking services that are not

traditionally used in the banking sector. These services include

• Electronic banking,

• PC banking,

• Telephone banking,

• Home banking and

• Internet banking.

Table 2.1 below contrasts the various features and functions offered by the alternative

forms of online ba nking.

Table 2.1 Features and Functions

Features Telephone

banking

Self Service

terminals

ATMs

Internet

Banking

Withdrawals ü

Deposits ü

Balance enquires ü ü ü ü

Interim Statements (30

transactions) ü ü ü

Transfer funds ü ü ü ü

Cheque book orders ü ü

Change ATM card PIN ü ü ü

Stop payment of cheques ü ü ü

Rates ü ü ü

Stop orders ü ü

Source: FNB Brochure, [2001].

UNCTAD 1999 as cited by Jasimuddin, [2001] states that in the USA, Internet

banking is growing at an annual rate of 60%, and that the number of Internet accounts

could reach as much as15 million by 2003. In 1999, one fifth of the Finish and

Swedish bank customers were online, and with numbers reportedly increasing

[UNCTAD 1999 as cited by Jasimuddin, [2001]



Internet banking is a remote home and/or office banking service that is offered to a

bank’s personal customers, to perform routine banking transactions through the

Internet [Standard Bank, 2001].

According to Bankrate.com [1998], Internet banking allows users to dial in and use

the bank’s own software or that of an Internet service provider. This type of banking

allows customers to access bank accounts from any location provided there is Internet

access [Absa Bank, 2001]. This provides customers with the ability to perform

transactions via the bank’s website with the advantage of not being required to visit a

physical branch or ATM (Automated Teller Machine).

The services available for Internet banking vary from bank to bank. According to

Bankrate.com [1998] virtually all banks that offer Internet banking services allow

consumers to check the balances in their accounts, transfer funds and order electronic

bill payments, with the even more sophisticated Internet banking systems allowing

customers to apply for loans, trade stocks or mutual funds, and even view actual

images of their checks or deposit slips.

2.1 ADVANTAGES OF INTERNET BANKING

Internet banking offers a certain advantages over the traditional banking methods.

Some of these advantages are as follows:

• Time saving – A customer can bank without physically visiting a branch.

• Convenience – Accounts can be paid and funds transferred without queuing or

writing out cheques.

• Accessibility – Services are available seven days a week, twenty-four hours a day.

• Confirmation- Transactions are executed and confirmed almost immediately.

• Range. Customers can do anything from checking on an account balance to

applying for a mortgage.

• Security – Customers can choose their own PIN, preventing unauthorised access

to their accounts.

• Safety – Reduces the need to carry large amounts of cash.



2.2 DISADVANTAGES OF INTERNET BANKING

Internet banking also has several disadvantages these include:

• Cost – Internet banking has certain systems requirements such as accessibility to

computers, computer type, memory, screen resolution and browsers, which prove

to be an additional cost to the customer when compared to traditional banking

methods or other online banking services such as ATMs.

• Cash availability - Currently, a customer cannot ma ke deposits or withdrawals

when using Internet banking.

• Security – This can also be a disadvantage as there is the threat from computer

hackers and fraudsters.

3. INTERNET BANKING IN SOUTH AFRICA Currently, South Africa's four main domestic banks, First National Bank (FNB),

Standard Bank, Nedbank and Absa are offering Internet banking services. These

banks are investing billions of rands on Internet banking to encourage customers to

adopt to this innovation.

According to SA.Internet.com [2001], Absa, predicts an Internet population of 3,2

million by the end of 2002, and plans to recruit 10 000 users to the service a month.

The bank has offered free Internet Service Provider service in order to encourage the

use of the Internet and Internet banking. The offer includes 5 email addresses and 10

MB of free web space. Absa hopes the publicity surrounding the service will generate

enough interest in Internet banking to double their customer base.

Absa currently has 153,000 customers who make use of online banking services, a

33% market share, second only to Standard Bank’s 35%. Nedbank has approximately

70,000 online users [Manson, 2001; SA.Internet,com, 2001].

Apart from the domestic banks, there is a new type of bank emerging in South Africa

and worldwide called the virtual bank. The major difference between the virtual bank

and other banks is the fact that a virtual bank does not have a physical presence, or a

brick-and-mortar building. Nevertheless, this bank performs most of the services

provided by the brick-and-motor banks with regard to Internet banking.



20twenty is South Africa's first virtual bank, and it has shown phenomenal growth

since it launched in July. 20twenty is a division of Saambou Bank. CEO Christo

Davel is confident that the virtual bank will achieve profitability within three years if

it meets its target of securing 15% of the market share [SA Computer Magazine,

2001].

According to a banking survey conducted by consultant group

PriceWaterhouseCoopers (PWC) in 2000, Standard Bank came out top in the category

of offering the broadest range of Internet banking services [sa.internet.com, 2000].

Standard bank allows customers to perform more transactions on the Internet than

Absa, FNB, Nedbank and 20twenty. With regard to Internet banking ra tes, Absa’s

monthly fee to individual customers is lower than that charged by the 3 other big

domestic banks: Standard Bank charges R19,50, First National charges R22,80 and

Nedbank charges R22,80.

Table 3.1 shows a comparison of the Internet banking rates charged by the domestic

banks.

Table 3.1: Internet Banking Rates

Service Nedbank ABSA Standard Bank FNB

Monthly service fee- Individual R22.80 R11.97 R19.50 R22.80

Business fee R22.80 R71.82 R19.50 R57.00

Internal transfer R1.60 R1.50 R2 R2.06

Balance enquires & statement Free First 5 free, after

R1 p/stat. Free Free

Source: Acuity Media Africa [2001].

Consumer acceptance and use of Internet banking in South Africa is still small,

[Manson, 2001]. This figure is small in comparison to the total number of banking

customers, or Internet users. Therefore, this study undertakes to provide greater

insight into consumer intentions to adopt Internet banking services.



4. RESEARCH FRAMEWORK

The research framework used for this study was based on one developed by Tan and

Teo [2000] in their study of factors affecting the adoption of Internet banking in

Singapore. The reason for the use of this framework was so as to be able to compare

results. The hypotheses were thus based on the Tan and Teo [2000] framework.

This framework is underpinned by the theory of planned behaviour developed by

Ajzen [1985] as well as the diffusion of innovations theory developed by Rogers

[1983] and was developed from a decomposed theory of planned behaviour

introduced by Taylor and Todd [1995].

The decomposed theory of planned behaviour provides a comprehensive way of

understanding factors that can influence a person’s decision to use information

technology. It postulates intention to adopt information technology is determined by:

• Attitude (which describes a person’s perception towards information technology)

• Subjective norms (which describes the social influence that may affect a person’s

intention to use information technology)

• Perceived behavioural control (which describes the beliefs about having the

necessary resources and opportunities to adopt information technology)

Intention according to Ajzen [1985] is the immediate determinant of to perform

behaviour. Thus intention to use a specific information technology can be seen as a

determinant of actual usage of that information technology. It was therefore decided

to measure intentions rather than actual usage of Internet banking as this factor is

more relevant when considering the adoption process.

The decomposed framework has been designed by Tan and Teo[2000] in order to

specifically examine the factors affecting Internet Banking as the information

technology of interest. Intention to adopt Internet banking was considered as the

dependant variable whilst Attitude, Subjective norms and Perceived behavioural

controls were considered to be independent variables. Diagram 4.1 displays the

framework.



Diagram 4.1 Framework For The Adoption of Internet Banking

Source: Tan and Teo, [2000].

4.1 VARIABLES AND HYPOTHESIS

The variables and hypothesis that form the Framework are displayed in table 4.2 and

discussed thereafter.

Attitude • Relative Advantage • Compatibility

Values Internet Experience Banking Needs

• Complexity • Trialability • Risk

Subjective Norms

Perceived Behavioural Control • Self-efficacy • Facilitating Conditions

o Availability of government Support

o Availability of Technical support

Intention to Use Internet Banking Services

Usage of Internet Banking Services



Table 4.2 Table of Hypothesises

H1A The greater the perceived relative advantage of using Internet banking services,

the more likely that Internet banking will be adopted.

H1B The greater the perceived compatibility of Internet banking with ones values,


H1C H1C: The greater the experience with using the Internet the more likely that

Internet banking will be adopted.

H1D The greater the use of banking products and services, the more likely that


H1E The higher the perceived complexity of using Internet banking, the less likely

that Internet banking will be adopted.

H1F The greater the trialability of Internet banking, the more likely that Internet

banking will be adopted.

H1G The lower the perceived risk of using Internet banking, the more likely that


H2 The beliefs associated with subjective norms are significantly related to an

individual’s intention to adopt Internet banking.

H3A The greater the self-efficacy towards using Internet Banking the more likely that


H3B The greater the extent of perceived technological support for Internet banking,


H3C The greater the extent of perceived government support for electronic

commerce, the more likely that Internet Banking will be adopted.

Source: Tan and Teo, [2000].

Attitude: According to Gibson, Ivancevich and Donnelly [2000] an attitude is a

positive or negative feeling or mental state of readiness, learned and organized

through experience, that exerts specific influence on a person’s response to people,

objects and situations.

According to Rogers [1983] as cited by Agarwal and Prasad [1998] the different

dimensions of attitudinal belief can be measured by using five attributes: Relative

advantage, compatibility, complexity, trialability, and observability. Observability is



excluded from the framework on the basis that privacy is an important aspect of

performing Internet banking and thus the possibility of observing individuals

performing banking is rather difficult. The different dimensions of attitudinal belief

used in this study are discussed below.

Relative advantage: Agarwal and Prasad [1998] show that relative advantage of an

innovation is positively related to its rate of adoption. This construct is similar to the

perceived usefulness in the Technology Acceptance Model, defined as the degree to

which a person believes that a particular information technology would enhance his or

her job performance. It has also been shown to be a contributing factor towards

Internet adoption. [Leaderer, Maupin, Sena and Zhuang, 1999]

It is therefore possible to suggest that the advantages that Internet banking offer over

and above regular banking methods could affect its rate of adoption. For example, the

possibility of performing transactions at any time of the day from any location with

Internet access would be a source of real advantage to people who have extremely

tight schedules. This provides support for the hypothesis

H1A: The greater the perceived relative advantage of using Internet banking

services, the more likely that Internet banking will be adopted.

Compatibility: The term compatibility refers to the fact that an innovation is more

likely to be adopted when it is compatible with an individuals job responsibilities and

value system [Agarwal and Prasad,1998]. Therefore the more an individual uses the

Internet and the more he or she perceives the Internet as compatible with his or her

lifestyle, the more likely that individual will adopt Internet Banking This provides

support for hypothesis:

H1B: The greater the perceived compatibility of Internet banking with ones values,


Experience: Jiang, Hsu, Klein and Lin [2001] state that the more experienced an

Internet user is the more likely they are to adopt new Internet technologies.. The

Hypothesis formulated was thus



H1C: The greater the experience with using the Internet the more likely that Internet


Banking Needs: According to Goldman [2001], Internet ba nking offers customers a

comprehensive array of banking services, which help them manage multiple accounts.

Since individuals with multiple accounts are likely to require a large number of

financial services the usage of Internet banking could allow for the easier

management of such a number of accounts and financial services. The hypothesis was

thus formulated:

H1D: The greater the use of banking products and services, the more likely that


Complexity: Is defined as the degree to which an innovation is considered relatively

difficult to understand and use and has been found to negatively influence the

adoption of the Internet. [Cheung, Chang and Lai, 2000]. Complexity can be

considered as the exact opposite of Ease of Use in the Technology Acceptance Model,

which has been found to directly impact the adoption of the Internet [Lederer et al,

1999].Therefore the greater the requirement of technical skills the less likely an

individual would be to adopt a new technology. Consequently the adoption of Internet

banking is likely to be less when the level of complexity of the Internet banking

process is high. This lead to the following hypothesis:

H1E: The higher the perceived complexity of using Internet banking, the less likely

that Internet banking will be adopted.

Trialabilty: Rogers [1983] as cited by Agarwal and Prasad [1998] when stating that

potential adopters of a new technology who are allowed to experiment with it will feel

comfortable with it and thus be more likely to adopt it. This leads to the hypothesis

that:



H1F: The greater the trialability of Internet banking, the more likely that Internet


Risk: One of the major influencing factors around the establishment and use of

Internet banking is that of security. According to Liu and Arnett [1999] the need for

secure transactions are critical to the success of not only Internet banking but that of

any e-commerce related website. Consequently the lower the perception of risks

involved in using Internet banking the more likely an individual would be prepared to

use it. Thus the hypothesis formulated was:

H1G: The lower the perceived risk of using Internet banking, the more likely that


Subjective Norms:

Refers to “a person’s perception that most people who are important to him think he

should or should not perform the behaviour in question” (Fishbein and Ajzen [1975]

as cited by Venkatesh and Davis [2000]. Cheung et al [2000] state that the Internet is

such a broadly discussed topic that social pressure plays an important part in

explaining its usage. It follows, therefore, that Internet banking may also be affected

by social pressures. Social pressures can emanate from any group such as parents,

colleagues and even friends. Whilst it would be difficult to predict how a particular

group could influence an individual in the adoption of Internet banking it is never the

less possible to assert that there is some influence by others on an individuals

intention to adopt Internet banking. The hypothesis is thus:

H2: The beliefs associated with subjective norms are significantly related to an

individual’s intention to adopt Internet banking.

Perceived Behavioural Control:

Refers to the factors that may promote the performance of a particular behaviour. This

definition has two components [Taylor and Todd, 1995]. The first component is that

of self-efficacy.



Self-efficacy: refers to the belief of an individual in his ability to perform a specific

behaviour. Taylor and Todd [1995] state that that the higher the level of self-efficacy

the more likely the adoption of an information technology. Thus in the case of

Internet banking it would be that an individual who is confident in his technical

computer skills would be more likely to adopt Internet Banking. Thus the hypothesis

was formulated that:

H3A: The greater the self-efficacy towards using Internet banking the more likely that


The second component is that of facilitating conditions.

Facilitating conditions: refers to objective factors in the environment that make an

act easy to perform.[Cheung et al, 2000]. In terms of Internet usage this would refer to

technological resources and infrastructure that are available. Thus the perception of

South Africans as to the quality of Internet infrastructure within South Africa could

affect the attitude of individuals towards adopting Internet Banking. The hypothesis

formulated is thus:

H3B: The greater the extent of perceived technological support for Internet banking,


Tan and Teo [2000] cited by Goh [1995] as stating that government can influence the

adoption of new technologies depending on the level of support that they provide.

Whilst it is not in the scope of this study to measure the level of commitment and

support by the South African government, it is possible to measure the perception of

individuals as to the level of support. The greater the level of government support

perceived by an individual the more likely he or she would be to adopt Internet

Banking. The hypothesis thus formulated is:

H3C: The greater the extent of perceived government support for electronic

commerce, the more likely that Internet Banking will be adopted.



5. RESEARCH DESIGN

An online questionnaire was used to collect empirical data for this study. This was

considered to be most appropriate, because the questions are only relevant to Internet

users and the hypotheses are best tested using a sample of this group rather than the

general South African population. A few hardcopies of the questionnaire were also

prepared, but it was found that due to the length of the questionnaire, it took a long

time to get a small number filled in. Capturing the data from the hard copies was also

very time consuming, so the online method was certainly more effective with a

questionnaire of this size.

The aim of the study was to collect South African data in order to test out the

hypotheses regarding the factors, which affect adoption of Internet banking and

compare these results with those collected in other countries. In order to ensure that

our results are directly comparable, it was decided that the same questionnaire that

was used in the Tan & Teo study [2000] done in Singapore would be used, with some

minor adjustments to account for local conditions.

The reasons for limiting the study to South African responses only include the

following:

• There are a huge numbers of Internet users worldwide, making it impractical

to survey

• If done worldwide, there would be more complexity, e.g: whether enough

responses have been received from different regions, relative numbers of

Internet users in different regions

• In order to be able to use the results to compare South Africa to other studies

done elsewhere – specifically those collected in Singapore.

Another advantage of using the questions from the Singapore study is that they have

already been tested in terms of how suitable they are for a university study.

Having been used successfully in a previous study, the questions were less likely to

cause confusion and had effectively been put through a very thorough pilot test.

However, another pilot study was deemed appropriate to account for different

conditions in the two countries. This involved testing the questionnaire out with 3 test



subjects, two male and one female who felt that whilst very long, the questionnaire

was generally understandable.

5.1 THE QUESTIONNAIRE

The questionnaire consists of three sections:

Section 1 gathers information about banking habits and Internet usage. Section 2

consists of two parts. The first obtains views on feelings about the Internet, and the

second asks respondents about their feelings towards the use of Internet banking.

The third section gathers demographic information. The 7 point Likert scale is used

for most questions, ranging from “1 - strongly disagree” to “7 – strongly agree”.

The following two tables [Tan & Teo, 2000] show the variables and items used, that

are relevant to the hypotheses:

Table 5.1. Attitude Variables

Variable Item Description

RELADV1 Internet banking makes it easier for me to conduct my banking transactions.

RELADV2 Internet banking gives me greater control over my finances.

RELADV3 Internet banking allows me to manage my finances more efficiently.

RELADV4 Internet banking is a convenient way to manage my finances.

RELADV5 Internet banking allows me to manage my finances more effectively.

Relative Advantage (RELADV)

RELADV6 I find Internet banking useful for managing my financial resources.

COMPAT1 Internet banking is compatible with my lifestyle.

COMPAT2 Using Internet banking fits well with the way I like to manage my finances.

Compatibility with Values (COMPAT)

COMPAT3 Using the Internet to conduct banking transactions fits into my working style.

INTUSE1 Span of Internet usage.

INTUSE2 Frequency of Internet usage.

INTUSE3 Intensity of Internet usage.

INTUSE4 Diversity of Internet usage.

INTSKILL1 I am very skilled at using the Internet.

INTSKILL2 I consider myself knowledgeable about good search techniques on the Internet.

Internet Experience(INTEXP)

INTSKILL3 I know less about using the Internet than most users. (R)



INTSKILL4 I know how to find what I want on the Internet using a search engine.

Banking Needs (BNKNDS)

BNKNDS Number of banking products and services currently using.

COMPLEX1 Using Internet banking requires a lot of mental effort.

COMPLEX2 Using Internet banking can be frustrating.

Complexity (COMPLEX)

COMPLEX3 Internet banking is an easy way to conduct banking transactions. (R)

TRIAL1 I want to be able to try Internet banking for at least one month. Trialability (TRIAL)

TRIAL2 I want to be able to sue Internet banking on a trial basis to see what it can do.

RISK1 I am confident over the security aspects of Internet banking in SA. (R) Risk (RISK)

RISK2 Information concerning my Internet banking transactions will be known to others.

(R) - Reverse-scored item

RISK3 Information concerning my Internet banking transactions can be tampered with by others. (R)

Source: Tan & Teo (2000) Pgs. 16 -17

Table 5.2. Perceived behavioural control variables

Variable Item Description

SELFEFF1 I am confident of using Internet banking if I have only the online instructions for reference.

SELFEFF2 I am confident of using Internet banking even if there is no one around to show me how to do it.

SELFEFF3 I am confident of using Internet banking even if I have never used such a system before.

SELFEFF4 I am confident of using Internet banking if I have just seen someone using it before trying it myself.

Self-efficacy (SELFEFF)

SELFEFF5 I am confident of using Internet banking if I have just the online "help" function for assistance.

GOVSUPP1 The government endorses Internet commerce in Singapore.

GOVSUPP2 The Singapore government is active in setting up the facilities to enable Internet commerce.

Government Support (GOVSUPP)

GOVSUPP3 The Singapore government promotes the use of the Internet for commerce.

TECHSUPP1 Advances in Internet security technology provide for safer Internet banking.

TECSUPP2 Faster Internet access speed is important for Internet banking.

Technology Support (TECHSUPP)

TECHSUPP3 Internet technology, like the Singapore ONE network, makes Internet banking more feasible.

Source: Tan & Teo [2000] Pg. 18

Before using the above questions on our questionnaire it was decided to email Tan &

Teo at the National University of Singapore to obtain their permission. It was also



hoped that they might have some advice or comments regarding their study to pass on

to us. Permission to use the questions was granted, and an acknowledgement of this

was included on our online questionnaire page. The final questionnaire used required

a few changes where a banking product was not offered in South Africa. One

question was added to Section 2B (perceptions of Internet banking) which read:

“Cell phone technology like WAP, makes Internet banking more feasible.”

This question was thought to be very relevant to the current South African business

environment, and it replaced a question which SA users did not really understand

during the pilot study-testing phase.

5.2 HOSTING

The questionnaire was hosted on the commerce faculty’s server, under the

information systems resources section. Unf ortunately, whilst the page was up and

running very soon, problems with the JavaScript validation delayed the actual

collection of data by almost two months. It was felt that JavaScript validation to check

that the respondents had answered all the questions was essential with a questionnaire

of this size, as users tended to get bored and try to skip sections. Results from each

submit were posted to a comma separated text file on the server.

5.3 SPONSORSHIP

In order to provide an incentive for people to answer the questionnaire a sponsor was

sought out. All of the South African banks currently providing banking services

online were contacted and asked if they would like to provide a small prize for the

respondents. In exchange, we offered to put their logo on the questionnaire, the bank’s

name on all of our advertising, and provide them with this paper when the research

was completed.

Two banks expressed an interest - Standard Bank and 20twenty. Both were relatively

slow to come to a decision on whether they would be willing to sponsor the study.

Eventually 20twenty agreed to sponsor a R200 prize for one of our respondents.

Following this we included 20twenty’s logo on the page and changed the colours to

be in line with their company colours.



5.4 ADVERTISING

In order to promote the questionnaire and attract respondents, a strategy of mailing

employees in medium to large companies was used. It was hoped that these emails

would be forwarded on to friends in other companies, thus increasing the total number

of responses.

Some university advertising was also carried out, including posters in major computer

labs on UCT campus, and personal emails to those email addresses that we could get

access to. Unfortunately sending out an email advert to a large group of students

proved impossible despite the prize on offer due to regulations surrounding the use of

campus email.

A once off advert was also placed in local newsgroups and in the Cape based

classified ads paper “Cape Ads”. The combination of the different forms of

advertising had the advantage of reaching a wide range of people, both from the

working and academic world.

5.5 RESPONSE RATE

When the time period allocated to data collection had elapsed, a total of 102 responses

had been recorded. This was significantly lower than the figure of 637 responses

recorded by Tan & Teo [2000].

The relatively low response rate can be attributed to several factors. Firstly, the

questionnaire was very long, and during pilot testing, users consistently complained

that it took too long to fill in all the questions. Several emails were also received from

respondents who did not finish filling out the questionnaire as they felt it was taking

too much time. This suggests that many of those who did begin filling out the

questionnaire abandoned it halfway. Due to the client side validation included on the

html page, it was not possible to submit the questionnaire if it was only half filled in.

Another important factor was the period during which the questionnaire was available

to record results. Due to problems with the JavaScript validation, completion of the

questionnaire was delayed, and it was only available to record results for a total of 3

weeks. The prize offered did provide some incentive, but was inferior to the 300

phone cards valued at $2 each for the first 300 respondents offered by Tan & Teo

[2000], which was a more effective in attracting large numbers of respondents.



5.6 RELIABILITY AND VALIDITY ASSESSMENT

Prior to analysis of the results using linear regression, the research instrument was

tested for its reliability and validity. To check for convergent and discriminate validity

of the constructs, factor analysis was used, and in conjunction with this the

Cronbach’s Alpha test was used to test for reliability (see table 3). The results of the

factor analysis are shown in the Appendix, and in general the results show that both

discriminant validities are satisfied. There are also no apparent problems with

heteroskedasticity or multicollinearity, details of which can also be found in the

appendix.

A minimum Cronbach’s Alpha value of 0.6 indicates reliability of constructs. The

Cronbach’s Alpha values for all constructs exceeded 0.6 except that of Internet

experience and skill at 0.5783. This construct presented some problems due to the fact

that it is essentially a combination of two distinct constructs, experience and skill. The

final construct used includes 3 experience variables and one variable which is the

average of the 4 skill variables. This configuration was used for 3 reasons. Firstly, this

allows experience and skill to load onto one factor and thus be used more effectively

for testing the hypothesis relating to Internet experience and skill. Secondly, this

arrangement actually provides a Cronbach’s Alpha larger and closer to 0.6 than the

two sets of variables considered separately. Thirdly, this is apparently the technique

used by Tan & Teo (2000) when producing their results. In order to be able to make

use of the experience and skill data at all, it was decided to use this arrangement of the

variables despite the Cronbach’s Alpha being slightly below the suggested 0.6.

Table 5.3 Reliability Analysis using Cronbach’s Alpha

Variable Cronbach's Alpha Relative advantage 0.9564 Compatibility with values 0.9436 Internet experience and skill 0.5783 Complexity 0.6282 Trialability 0.9502 Risk 0.9389 Subjective norms 0.8212 Self-efficacy 0.9152 Government support 0.8231



6. FINDINGS AND ANALYSIS

The respondents demographics profile is presented in Table 6.1 and graphically

represented in Appendix B.

Table 6.1 Demographic Profile of Respondents

Frequency Percent

Gender Male 55 53.9%

Female 47 46.1% Age:

under 20 1 1.0% 20-29 76 74.5% 30-39 10 9.8% 40-49 10 9.8% 50-59 4 3.9%

Over 59 1 1.0% Highest Education :

Bachelor's Degree 54 52.9% Primary School 0 0.0%

Secondary School 20 19.6% Junior College 2 2.0% Masters Degree 11 10.8%

Doctorate Degree 3 2.9% Other 12 11.8%

Current Profession Student 53 52.0%

Professional 11 10.8% Academic 17 16.7%

Self-employed 1 1.0% Manager 5 4.9% Executive 2 2.0% Technician 1 1.0%

Retiree/Housewives 1 1.0% Other 10 9.8%

Income Less than R2000 46 45.1%

R2000-3999 8 7.8% R4000-4999 5 4.9% R5000-9999 15 14.7%

R10 000 - 14 999 14 13.7% R15 000 or more 11 10.8%

Not Answered 3 2.9% The number of male Internet users as opposed to female users is almost even with

male users being 53.9% and females 46.1% of the respondents. This represents a more

balanced sample than that of Tan and Teo [2000] in which the number of male

respondents was as high as 80%.



The respondents were relatively young with 74.5% of respondents being between the

ages of 20-29. This is consistent with Tan and Teo’s [2000] study in which 64.1% of

respondents were between 20-29 and supported by Teo and Lim’s [1999] findings that

the majority of Internet users are youths and young adults.

The majority of respondents were those with Bachelors degrees (52.9%) followed by

those with secondary level of education (19.6%). As with Tan and Teo’s [2000] study

the indication is that respondents are generally of sound educational background

In terms of professions most respondents were students (52%). Of those earning an

income the majority earned an income less than R2000 per month. This is attributed

to the high level of respondents that were students.

Eleven hypotheses were formulated for the study, but the one relating to technology

support (H3B) was removed from the analysis following factor analysis as it did not

load as a single factor. In order to test the remaining 10 hypotheses, linear regression

was used, regressing each of the independent variables on to intention to adopt as the

dependent variable. The results are shown in table 5

Table 6.2 Results of Linear Regression

Factor Hypothesis Variable Beta p-value Attitude H1A Relative advantage 0.582 0.000 H1B Compatability with values 0.605 0.000 H1C Internet Experience/skill 0.539 0.003 H1D Banking Needs 0.100 0.075 H1E Complexity -0.343 0.001 H1F Trialability 0.197 0.005 H1G Risk -0.221 0.004 Subjective Norms H2 Subjective Norms 0.049 0.567

H3A Self-Efficacy 0.500 0.000 H3B Technology Support n/a n/a

Percieved Behavioural Control H3C Government Support 0.154 0.172

As seen in the table, the only hypotheses which are not supported are H1D (banking

needs), H2 (subjective norms), and H3C (government support).



The support for H1A (relative advantage) and H1B (compatability with values) is in

line with the results found by Tan & Teo (2000). This shows quiet conclusively that

across different populations, perceived relative advantage has a positive influence on

the adoption of Internet Banking and that Internet users who feel that Internet banking

is compatible with their values are more inclined to adopt.

Support for H1C is also in line with the results of Tan & Teo (2000). This shows

strong support for the suggestion that users who are more experienced at using the

Internet are more likely to adopt than those who have not had much exposure to it.

The first difference between the SA and Singapore results occurs with the rejection of

H1D (banking needs). Tan & Teo accepted this hypotheses, but the data collected in

this study does not support it. In other words there was no evidence to suggest that the

more banking products a respondent was currently using the more likely he or she was

to adopt Internet banking. A possible reason for the rejection of this hypothesis might

be the list of banking products provided on the questionnaire for respondents to

choose from. The list was taken from the original questionnaire used in Singapore,

and whilst banking products are relatively similar, the list may have omitted some

banking products relevant to the SA banking consumer and adversely affected the

results.

H1E (Complexity) is accepted, and whilst this is in contrast to the results obtained by

Tan & Teo (2000), it is in agreement with a wide range of previous findings (Cheung

et al, 2000; Lederer et al 1999). All of these sources suggest that the more complex a

new technology is perceived to be, the less likely it is that it will be adopted.

The acceptance of both H1G (risk) and H1F (trialability) is in agreement with the

findings of Tan & Teo (2000) as well as a range of other sources (Agarwal and

Prasad, 1998). Similarly, the acceptance of H3A (self-efficacy) is in agreement with

Tan & Teo, further adding support to the argument that users who are confident of

their abilities to use Internet banking will be more likely to adopt such services.

Another point on which both sets of results strongly agree is the strong rejection of

hypothesis H2 (subjective norms). The results of this study provide an even higher p-



value than that obtained in Singapore for this hypothesis and this indicates that

adoption is not affected by subjective norms. In other words, opinions of friends,

family or peers are not considered an important factor when deciding whether to

adopt Internet banking services. A possible reason for this is that Internet banking

services are seen as an extension of other banking services. Whilst the decision to

bank at a particular bank may be affected by subjective norms or what peers might

think of that bank, once the bank has been accepted, the decision to adopt an

additional service at this particular bank (i.e: Internet banking) would be unaffected

by peer opinions. Another possible explanation is that information regarding Internet

banking is widely available online and on the bank’s sites, reducing the need for

potential adopters to seek out the opinions of friends and family.

The final hypothesis, H3C (Government support) is rejected, but was accepted by Tan

& Teo (2000). This is interesting as it suggests a difference in the way in which

respondents in SA and Singapore regard the views of the government. This ma y

indicate that government image differs between the two countries. Possibly

respondents simply felt unsure of what the governments position is on issues such as

e-commerce or Internet security.

6.1 PREFERRED INTERNET SERVICES AND PRODUCTS

Table 6.3 shows a range of possible Internet Banking products, and how useful

respondents felt that they were. Most respondents rated Account information as the

highest or most important product, followed by bill payments, fund transfers, and

generation of transaction summary reports. Other suggested useful products/services

included financial advice, handling fixed deposit accounts, tools to aid in reducing

bank charges, and information relating to fees/interest rates.

Respondents seemed relatively indifferent regarding cheque functionality, and the

lowest scores included financial planning, loan applications and share margin trading.

These results are remarkably similar to those obtained by Tan & Teo (shown below in

table 6.4). In fact the means of the top two (account information and electronic bill

payments) are almost identical, and the order is identical except for one swap around.

This is very interesting to see, as there is a clear indication that the banking products



or services considered most important to the consumer do not differ between the two

countries despite apparent differences in culture.

Both sets of data had a high percentage of student respondents, who may have

influenced the popularity of financial planning analysis and share margin trading –

both products that would be less important to extremely low -income earners like full

time students.

Table 6.3 Internet Banking Services Variable Internet Banking Service Mean* Std Deviation

IBNKSVCa Account info 6.471 1.054

IBNKSVCb Electronic bill payments 6.078 1.460

IBNKSVCc Funds transfer 5.912 1.609

IBNKSVCf Generate summary reports of bank transactions 5.353 1.872

IBNKSVCh Cheque cancellation 4.510 2.013

IBNKSVCg Chequebook application 4.059 1.851

IBNKSVCe Financial planning and analysis 3.725 1.810

IBNKSVCd Loan application 3.657 1.922

IBNKSVCi Share margin trading 2.990 1.943

*Respondents rated usefulness using scale of 1(not useful) to 7(very useful)

Table 6.4 Tan & Teo’s results - Internet Banking Services Variable Internet Banking Service Mean* Std Deviation

IBNKSVCa Account info 6.54 0.94

IBNKSVCb Electronic bill payments 6.13 1.30

IBNKSVCf Generate summary reports of bank transactions 5.78 1.51

IBNKSVCc Funds transfer 5.63 1.60

IBNKSVCh Cheque cancellation 5.59 1.65

IBNKSVCg Cheque book application 5.41 1.65

IBNKSVCe Financial planning and analysis 4.48 1.92

IBNKSVCd Loan application 4.38 1.92

IBNKSVCi Share margin trading 4.09 2.00

Source: Tan & Teo (2000) pg.28



When the criteria used for selecting an online bank were examined (table 7.1), the

results were once again very similar to those obtained in Singapore (table 7.2)

In both cases, the bank’s reputation and the variety of services offered were ranked

number one and two respectively. Tan & Teo [2000] omit the familiarity wit h the

bank from their results, but the order of the remaining factors is identical in both sets

of results. Clearly whether the bank is owned locally or internationally is not

important when considering adoption.

Table 7.1 Criteria for Selecting an Online Banking Service Variable Criteria Mean* Std Deviation

IBNKCRIe Reputation of the bank 6.480 0.841

IBNKCRIf Variety of services offered by the bank 5.863 1.357

IBNKCRIa Familiarity with the bank 5.657 1.472

IBNKCRId Size of the bank 4.618 1.490

IBNKCRIb Ownership of the bank- Local 4.167 1.904

IBNKCRIc Ownership of the bank- Overseas 3.373 1.757

Table 7.2 Tan & Teo’s results - Criteria for Selecting an Online Bank Service

Variable Criteria Mean* Std Deviation

IBNKCRIe Reputation of the bank 6.33 0.98

IBNKCRIf Variety of services offered by the bank 6.31 0.95

IBNKCRId Size of the bank 5.53 1.39

IBNKCRIb Ownership of the bank- Local 4.84 1.84

IBNKCRIc Ownership of the bank- Overseas 4.31 1.83

Source: Tan & Teo (2000) pg.28

Not surprisingly, respondents were most likely to adopt Internet banking of it was

free. This particular option was part of Tan & Teo’s (2000) options, and we left it in

for completeness, even though the result obtained is predictable and banks are

unlikely to consider offer ing the services completely for free. (See table 7.3)



The second most popular option is that of a flat fee per month, followed by a flat fee

plus per transaction fee. Once again, the order of importance is identical to the

Singapore results, which have not been included here.

Table 7.3 Preferred Internet Charges

Variable Charging Scheme Mean* Std Deviation INTENTCd no fee for using Internet banking 6.598 1.074 INTENTCa A flat fee per month for using Internet

banking. 5.265 1.712

INTENTCb A flat fee per month plus a fee per transaction for using Internet banking.

3.265 1.829

INTENTCc a time-based usage fee for using Internet banking

3.176 1.804

One very interesting difference between the results lies not in the order of the options,

but the means. Whilst the mean for “no fee” was virtually identical, the means for the

flat fee and the flat fee plus transaction fee were 2.45 and 1.76 respectively in the

Singapore study. These very low means showed that respondents in Singapore were

unlikely to adopt Internet banking as soon as there were costs involved. South African

respondents were far more willing to pay for online banking services, and were in fact

quite likely to adopt Internet banking if only a flat fee per month was charged.

At present the only options available from South African banks are fee structures

which include both a monthly fee and a per transaction charge. Based on the figures

above, it would appear that per transaction charges are a big deterrent to adoption and

as so whilst it is impossible to eliminate them, banks should aim to minimise them.

In terms of the characteristics of the website, the factors considered important were:

• Clear, comprehensible instructions which are easy to read

• Prompt processing of transactions

• Communication with service personal by phone or fax in the event of a problem

• Must be able to connect quickly and download time must be short

Resemblance of the site to the physical bank was considered only moderately

important, as was the extent to which the site was interactive and the ability to

customise the display of the site.



Music, animation and other entertainment features were considered by respondents to

not be useful.

7. LIMITATIONS OF THIS STUDY

Whilst every effort was made to make this study as com prehensive as possible, certain

limitations were present:

• The response rate was lower than was hoped for, and this is mainly attributed to

difficulties in advertising the questionnaire. Organisations that were approached

were extremely reluctant to send out bulk emails to personnel. Due to the low

response rate, the results are less statistically significant, and this may mean that

some of the rejected hypotheses would have been accepted had the sample sizes

been much larger, and significance levels thus higher.

• The second limitation was the fact that the questionnaire was designed for

respondents who had not adopted Internet banking. Respondents who had already

adopted Internet banking may thus have found certain questions irrelevant or

difficult to understand. Consequently the answers to certain questions in this

regard may have been answered inaccurately. Tan & Teo (2000) assumed that

most users had not yet adopted when formulating the questions, but in the case of

this study it would have been more appr opriate to modify the sections relating to

adoption to provide options for those who had already adopted.

• The third limitation was the fact that a large number of the respondents were

students. This could be attributed to the fact that the most accessible demographic

group in terms of possible respondents was students, and the most effective

advertising options available were those targeting students This did however limit

the variety of results and thus the value of the scope of the findings.

• By offering a R200 prize, results may have been skewed slightly towards those

Internet users who consider a R200 prize to be worth the effort of answering the

questionnaire. This is supported by the data, which shows a high portion of low

income earners (although it is worth noting that students, who received most of

the advertising are generally low income earners).



8. DISCUSSIONS AND CONCLUSIONS

The results show that intention to adopt Internet banking can be predicted by

attitudinal factors, perceived behavioural control factors to a lesser degree, and not by

subjective norms. All attitudinal factors except banking needs are found to be

significant, with complexity and risk showing a negative relationship.

Possible reasons why banking needs are not related include the design of the

measuring instrument. Possibly not enough was done to localise the list of banking

products provided for respondents to choose from.

The opinions of friends, family and other consumer-relevant groups are found to have

no significant relationship with the intention to adopt Internet banking. This is in

agreement with the results obtained in Singapore, and suggests that the opinions of

peers is not important with an innovation such as Internet banking. Availability of

information about Internet banking online may reduce the need for potential adopters

to seek out the opinions of peer groups.

Of the three perceived behavioural control factors, technology support could not be

included as a factor do to inadequate factor loading, and so could not be tested. Self-

efficacy was found to be an influential factor, and government support was not.

Support for self-efficacy is in agreement with the results obtained in Singapore,

whereas rejection of government support is in contrast with them. Possible reasons for

this would include differences in how government is viewed in the two countries and

may also include differences in how government policy relates to online security and

e-commerce in the two countries.

The findings of this study are of interest for two different reasons. Firstly, they

provide a set of South African results which can be directly compared with those

obtained previously in Singapore. Secondly, the study provides information of interest

to the South African banking industry.

In cases where the findings from the two countries have supported each other, this is

able to lend even more credibility to the acceptance of the hypotheses, and in cases

where the findings contradict the earlier study, insights into the effects of different



conditions across the countries obtained. The results obtained in this study are

remarkably similar to those obtained in the previous study. Of the eight hypotheses

tested, only 3 contradicted the Singapore study, and in terms of the usefulness of

Internet banking products and the criteria used for selecting a bank, the results were

almost identical. These results by and large validate those of the previous study.

Differences to note for SA banks relate to the 3 hypotheses which were not in

agreement with the results obtained in Singapore. The rejection of H1D (banking

needs) may be related to the instrument, but banks could conclude that their Internet

banking services should be marketed to all customers, rather than those who are

extensive users of a wide range of banking products.

Similarly, as complexity is found to have a negative effect on the intention to adopt

Internet banking services, banks should aim to make their Internet banking services as

simple and easy to use as possible so that potential adopters do not perceive them as

being complicated or difficult to use.

The rejection of H3C (government support) is not particularly relevant to the banking

industry, but may be of Interest to government policy makers, and non governmental

organisations aiming to promote IT and e-commerce such (e.g: CITI).



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APPENDIX A – FACTOR ANALYSIS

Factor analysis was applied to the results in line with the analysis used in by Tan &

Teo (2000). Similarly, variances with loadings greater than 0.4 were accepted. Two

rounds of factor analysis were performed in order to produce the end results shown in

Table A1.

The analysis produced 8 factors with eigenvalues greater than 1.0, and these 8 factors

explained 77.74% of the variance. Unfortunately, in order to isolate these 8 factors,

some of the data had to be excluded. This was due to cross loading, caused in

particular by the three variable relating to tech support. It was decided to remove the

tech support variables from all further analysis. Similarly, COMPLEX3 and RISK1

were removed due to conflicts with other factors.

The results show some similarity with those of Tan & Teo (2000). All of the items

measuring compatibility with values loaded together with those measuring relative

advantage. Tan & Teo (2000) suggest that respondents who perceived banking on the

Internet as compatible with their values might tend to view them more favourably,

and hence would be more likely to perceive Internet banking services as an

advantageous innovation.

The results of the factor analysis suggest that the conditions of convergent and

discriminant validity are satisfactorily met (with minimum factor loadings of 0.4)



Table A1. Factor Analysis

Factor 1 Factor 2 Factor 3 Factor 4 Factor 5 Factor 6 Factor 7 Factor 8 EXP1 0.040 -0.046 0.110 -0.196 0.150 -0.131 0.593 0.168 EXP2 -0.004 0.019 -0.052 0.034 -0.054 0.006 0.881 -0.074 EXP3 0.015 0.207 0.053 0.058 0.008 0.208 0.505 -0.089 SKILL 0.134 0.385 -0.020 0.107 -0.065 0.309 0.497 0.224 RELADV1 0.823 0.133 0.085 0.027 -0.064 0.110 0.076 -0.029 RELADV2 0.812 0.242 0.047 0.141 0.100 0.102 -0.065 0.213 RELADV3 0.910 0.164 0.056 0.074 0.075 0.028 0.011 0.151 RELADV4 0.915 0.193 0.046 0.040 0.049 0.014 0.038 0.126 RELADV5 0.833 0.205 0.062 0.176 0.096 0.061 -0.109 0.117 RELADV6 0.847 0.183 0.129 -0.032 0.145 0.082 -0.048 0.088 COMPAT1 0.783 0.288 0.063 -0.040 0.057 0.080 0.149 -0.166 COMPAT2 0.867 0.260 0.032 -0.002 0.101 0.048 0.041 0.056 COMPAT3 0.893 0.218 0.000 0.019 0.054 0.068 0.111 -0.001 COMPLEX1 -0.181 -0.166 0.038 -0.001 0.060 -0.172 -0.188 -0.766 COMPLEX2 -0.098 -0.118 -0.039 -0.046 -0.062 -0.066 0.117 -0.812 TRIAL1 0.146 0.074 0.049 0.945 0.057 0.008 -0.044 -0.005 TRIAL2 0.060 -0.041 0.068 0.963 0.098 0.032 0.017 0.061 RISK2 -0.135 -0.165 0.026 -0.041 -0.030 -0.915 -0.082 -0.149 RISK3 -0.178 -0.119 -0.009 0.004 -0.008 -0.928 -0.049 -0.084 GOVSUPP1 0.091 0.024 -0.029 0.169 0.796 0.098 0.214 0.003 GOVSUPP2 0.117 -0.008 -0.087 0.003 0.901 -0.015 -0.010 0.009 GOVSUPP3 0.124 -0.055 0.003 0.000 0.838 -0.042 -0.117 0.003 CONFI1 0.364 0.781 0.050 -0.064 -0.030 0.152 0.181 -0.028 CONFI2 0.359 0.784 -0.014 -0.101 -0.007 0.007 0.172 0.160 CONFI3 0.292 0.862 0.036 0.013 -0.074 0.074 0.050 0.104 CONFI4 0.285 0.810 -0.016 0.042 0.038 0.104 -0.079 0.071 CONFI5 0.308 0.799 0.010 0.135 0.016 0.091 0.068 0.101 SOCINF1 0.137 -0.027 0.884 -0.051 0.030 -0.074 -0.022 -0.020 SOCINF2 0.024 0.017 0.779 0.149 -0.135 0.082 0.057 0.092 SOCINF3 0.133 0.048 0.880 0.018 -0.005 -0.021 0.046 -0.061 Expl.Var 7.311 3.968 2.244 2.028 2.294 2.010 1.892 1.574 Prp.Totl 0.244 0.132 0.075 0.068 0.076 0.067 0.063 0.052 Eigenvalue 9.820 2.899 2.382 2.126 1.839 1.590 1.454 1.212 Variance % 32.734 9.664 7.939 7.087 6.130 5.301 4.846 4.040 Cum Var % 32.734 42.398 50.337 57.424 63.554 68.855 73.701 77.740

The next test required is Spearman’s rank correlation, testing for heteroskedasticity,

which is the occurrence of unequal variances.

Spearman’s R = 0.033, t = 0.331, and p > 0.05. For heteroskedasticity to exist, the

correlation coefficient should be significant (i.e: p < 0.05). This indicates that there is

no heteroskedasticity.



It is also necessary to test for multicollinearity in the data, which refers to high

correlations among the independent variables. Tan & Teo (2000) use the variance

inflation factor (VIF) computed for each independent variable to test for this. If the

VIF of a variable exceeds 10, that variable is highly collinear and will pose a problem

to regression analysis. Table A2 shows the variables and their VIF values. All the

values are well below 10, indicating no problem of multicollinearity.

It is interesting to note that the variables with the highest VIF values are Relative

advantage and compatability with values. This is to be expected as these are the two

variables which loaded onto one factor.

Table A2. VIF Values

Variable VIF Value

Relative advantage 4.393 Compatability with values 4.224 Internet experience and skill 1.289 Banking Needs 1.169 Complexity 1.217 Trialability 1.101 Risk 1.205 Subjective norms 1.064 Self-efficacy 1.746 Government support 1.098

Documents

ER009_Factors Affecting the Adoption of Internet Banking