EJISDC (2009) 37, 6, 1-31
THE ADOPTION AND USE OF
PERSONAL INTERNET BANKING SERVICES IN THAILAND
Surapong Prompattanapakdee
Assumption University, Thailand
Surapong@gmail.com
ABSTRACT
Based on existing theory and previous studies a theoretical model of the important variables
that affect the adoption and use of personal Internet Banking services is developed. Using
data collected by questionnaire from 618 respondents the theoretical model is analyzed and
simplified using structural equation modeling techniques. The findings represented in the
final model are: interpreted; compared to the findings from previous studies; and used to
draw conclusions which are of practical use for those involved in the development and use of
personal Internet Banking.
Keywords: Collectivism, personal characteristics, personal Internet Banking, personal
relationships, structural equation modeling, technology acceptance model, Thailand.
1.
INTRODUCTION
According to the Internet World Statistics (2008) the number of Internet users in Thailand in
2008 is approximately 13.4 million corresponding to a penetration rate of 21 percent and a
growth rate across the period 2000-2008 of 483 percent. Consequently, there is a significant
opportunity for increasing the adoption of Internet Banking services in Thailand. These
services were introduced in Thailand in 1999 in accordance with the Commercial Banking
Act B.E. 2505 by the Siam Commercial Bank Plc. and the first commercial Bank of Thailand
(BOT) which plays an important regulatory role in the banking sector and provides the
financial infrastructure to serve the needs of the business and financial sectors
(www.bot.or.th). Commercial banks are licensed and they may include a branch of a foreign
bank. In general, banks conduct their business in 2 main areas: wholesale banking which
provides services for financial institutions; and retail banking which provides banking
services for individuals including personal Internet Banking (PIB). All banks operate under
regulations set by the BOT and the Law of Thailand and over time the BOT has revised the
notification requirements for commercial banks regarding their provision of Internet Banking
services and has expanded the areas of use of Internet Banking. Commercial banks provide
corporate Internet Banking (CIB) services which are intensive and complex as well as
simpler PIB services. Thai banks are continuously looking to move low-value transactions
away from the branch counter to ATM networks and to Internet and telephone banking
(Chudasri, 2002). Consequently, most commercial banks in Thailand have launched web sites
and they are in the early stages of using the Internet as a new distribution channel. These
developments are in accordance with objectives set for the expansion of Internet based
services in the sequence of Thailand’s national ICT policies: IT-2000 (Years: 1996-2000);
ICT Master Plan (Years: 2002-2006); and IT 2010 (Years: 2001- 2010) which have been
discussed in detail by Winley et al. (2007).
Unfortunately, detailed up to date reporting on the development of Internet Banking
services in Thailand is very difficult to locate and is generally not available. However, based
on information from the BOT for the period 2000-2003 it is seen that over this period the
volume of Internet Banking transactions increased from 3.3 million per year to 10.33 million
per year corresponding to an annual average growth rate of close to 53 percent. The total
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value of all transactions in 2000 was 0.3 billion baht and this increased exponentially to 730.5
billion baht in 2002. From 2002 to 2003 the initial exponential growth slowed considerably
and the total value of all transactions decreased to 638 billion baht while the number of
transactions only increased by 0.34 million. Although the information is not up to date and it
is does not differentiate between CIB and PIB services it does indicate at least a moderate
level of acceptance of these services among banking customers in Thailand. In particular, in
2007 at the time when this study began the BOT estimated that there were 1.7 million
individuals using PIB services provided by banks in Thailand (www.bot.or.th). Based on this
estimate and the number of Internet users it appears as though approximately 15 percent of
Internet users also use PIB services.
From the websites of the 34 banks operating in Thailand in 2007 it was found that a
range PIB services were offered by 8 of the 17 Thai banks but by only 2 of the 17 foreign
banks. The standard PIB services considered in this study were offered by all of these 10
banks and included: account balances; fund transfer; bill payment; and summary reports.
Additional services such as stopping checks and international fund transfer are offered by
only a few of these banks and 3 Thai banks offered only account balances and summary
reports while the remaining 21 banks offered no PIB services at all. Each of the 10 banks
offering the standard range of services also advertised a range of associated security measures:
a login authentication secure socket layer; 128 bit encryption, a firewall, intrusion detection
software, automatic sign-off; and a VeriSign Secure system.
Against this background the study aims to develop a parsimonious causal model for
the use of PIB services in Thailand. Based on a comprehensive review of previous studies a
theoretical model is proposed which includes the important variables and the relationships
among them. Using data collected by questionnaire this model is then analyzed and
simplified using structural equation modeling (SEM) techniques. The findings represented in
the final model are interpreted, compared to findings from previous studies, and used to draw
conclusions which are of practical use for those responsible for the development and use of
PIB. In order to improve the presentation the reader is referred to parts of the Appendix for
details of statistical methods and outputs.
2.
RESEARCH DESIGN AND METHODOLOGY
The study aims to develop theoretical knowledge with practical implications based on an
individual’s perceptions of the importance of factors that determine their adoption and use of
PIB. The research is partly basic and applied and partly descriptive and explanatory. An
individual’s perceptions are measured at a single point in time using a self-administered
questionnaire and this cross-sectional field study approach has been used successfully in
previous studies of PIB (e.g. Eriksson et al., 2005) and is justified as an economical and
feasible approach for measuring complex variables in situations where the independent
variables cannot be manipulated and the influence of confounding variables cannot be
controlled as in experimental designs (Boudreau et al., 2001). Also, field studies identify
relatively strong effects on dependent variables and this enhances the statistical results. These
decisions regarding the research design followed the guidance provided by Neuman (2006).
The target population for the study is individuals who use the PIB services (account
balances, fund transfer, bill payment, and summary transaction reports) provided by the 10
banks discussed above. Using the BOT estimate of 1.7 million PIB users and the probability
sampling method recommended by Tryfos (1996) (Appendix A1) a random sample of size
600 was determined. It was not logistically possible to stratify the sample to represent the
proportions of PIB users at each of the 10 banks. Instead, users were accessed through
organizations as well as the researcher’s personal contacts. The sampling was done in stages
to ensure that a minimum sample size of 600 was achieved.
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English and Thai versions of the questionnaire were constructed and examined by a
focus group of 5 senior banking officials. Useful suggestions related to the translation of the
questions from English to Thai were incorporated into revised versions of the questionnaire.
The Thai version was then used in a pilot study with 10 PIB users. The feedback from
respondents resulted in some minor corrections to the layout and wording of a small number
of questions but the descriptive analysis of their responses did not suggest the need to make
any other changes. These minor changes were incorporated into both versions of the
questionnaire and the Thai version was used in the full study. A notated English version of
the final questionnaire is included in Appendix A2.
3.
LITERATURE REVIEW
The review is structured into 2 sections. The first section presents an overview of previous
studies of Internet Banking showing the country, the unit of analysis, and the focus for each
study, followed by a discussion of research approaches that have been used. The second
section discusses the main variables that have been included in previous studies. The purpose
of the review is to motivate the development of a theoretical model of the causes and effects
associated with the use of PIB.
3.1 An Overview of Previous Studies
The studies included in the overview presented in Table 1 relate to CIB or PIB and they were
selected from studies reported since 1999.
Table 1: Previous Studies by Country, Unit of Analysis and Focus
Country
United States of
America
United Kingdom
United Kingdom and
Republic of Ireland
Finland
Finland and Portugal
Denmark
Estonia
Ukraine
Italy
Greece
North Cyprus
Turkey
Spain
European Union
United Arab Emirates
Egypt
India
Bangladesh
Pakistan
Unit of Analysis
Small Business
Individuals
Individuals
Organizations
Focus of the Study
Urban/rural
mobile PIB
CIB
PIB
Bank staff and PIB
CIB
Individuals
PIB
Daniel (1999)
Individuals
Individuals
Organizations
Organizations
PIB
PIB
CIB
CIB
Individuals
PIB
Organizations
Individuals
Organizations
Individuals
Individuals
Individuals
Individuals
Individuals
Organizations
Banking
Organizations
Banking
Organizations
Individuals
CIB
Urban/rural PIB
CIB
PIB
PIB
PIB
PIB
PIB
CIB
Kuisma et al. (2007)
Kivijärvi et al. (2007)
Mols (2000)
Eriksson et al. (2008)
Eriksson and Nilsson (2007); Eriksson et al.
(2005)
Jennex and Amoroso (2002)
De Blasio (2008)
Anthanassopoulos and Labroukos (1999)
Jenkins (2007)
Polatoglu and Elkin (2001)
Casaló et al. (2007)
Guerrero et al. (2007)
Khanfar (2007)
Kamel and Hassan (2003)
Individuals
References
Drummond (2008)
Gehling et al. (2007)
Kolodinsky et al. (2004)
Durkin (2007)
Shah et al. (2007); Shah and Siddiqui (2006)
CIB and PIB
Malhotra and Singh (2007)
CIB and PIB
Alam et al. (2007)
PIB
Qureshi et al. (2008)
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Country
Unit of Analysis
Bank Managers
Singapore
Individuals
Individuals
Malaysia
Individuals
Organizations
Organizations
Thailand
Individuals
Taiwan
Individuals
Hong Kong
Individuals
South Korea
Individuals
Individuals
Australia
Individuals
Brazil
Individuals
Focus of the Study
References
CIB and PIB
Kaleem and Ahmad (2008)
Gerrard and Cunningham (2003); Tan and Teo
PIB
(2000)
Age factors (PIB) Amin (2007); Rugimbana (2007a, 2007b)
Lallmahamood (2007); Shun-Fong et al.
PIB
(2007); Ainin et al. (2005)
CIB
Suganthi et al. (2001)
Chiarakul et al. (2007); Rotchanakitumnuai
CIB
and Speece (2003, 2004); Larpsiri et al. (2002)
Jaruwachirathanskul and Fink (2005);
PIB
Ongkasuwan and Tantichattanon (2002)
PIB
Shih and Fang (2004)
Yiu et al. (2007); Cheng et al. (2006); Chan
PIB
and Lu (2004)
PIB
Suh and Han (2002)
Herington and Weaven (2007); Kam and
PIB
Riquelme (2007)
Age factors (PIB) Heaney (2007)
Botelho (2007); Hernandez and Mazzon
PIB
(2007)
It is seen from Table 1 that research interest in Internet Banking is worldwide and
studies have been conducted in nations with different levels of maturity with respect to the
use of information technologies and with economies at different stages of development. Most
attention has been given to PIB and less attention has been given to the more complex
situation of CIB. A few studies have focused on: the age of users; differences between urban
and rural users; and Internet Banking from the bank’s perspective but there is little evidence
of cross cultural studies. In Thailand most attention has been given to CIB and only 2 studies
relate to PIB.
Not all of the previous studies in Table 1 have set out to test a theoretical model.
Many would be described as exploratory or descriptive rather than explanatory. There is also
a mixed use of quantitative and qualitative approaches and case studies. Interviews and
questionnaires are used frequently to gather data. Table 2 gives examples of explanatory
studies where a theoretical approach was adopted.
Table 2: Theoretical Approaches Adopted in Previous Studies
Theoretical Basis
Focus of
the Study
Innovation and Diffusion
Theory
Critical Success Factors
Social Contagion Theory
The Decomposed Theory
of Planned Behavior
Technology Acceptance
Model (TAM)
Previous Studies
CIB
Eriksson et al. (2008)
CIB
CIB
Shah et al. (2007); Shah and Siddiqui (2006)
Shi et al. (2008)
PIB
Jaruwachirathanakul and Fink (2005); Shih and Fang (2004)
CIB
Kamel and Hassan (2003)
Amin (2007); Lallmahamood (2007); Yiu et al. (2007); Botelho (2007);
Shun-Fong et al. (2007); Cheng et al. (2006); Eriksson et al. (2005);
Chan and Lu (2004); Pikkarainen et al. (2004); Wang et al. (2003); Suh
and Han (2002)
PIB
It is seen from Table 2 that the TAM, or a modified version of it, has been used often
as the theoretical model to study the adoption and use of PIB. This is not surprising given that
the goal of the TAM is to provide an explanation of the determinants of acceptance that is
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general and capable of explaining user behavior across a broad range of end-user computing
technologies and user populations while at the same time being both parsimonious and
theoretically justified (Davis et al., 1989). However, there are no previous studies of PIB in
Thailand which have adopted the TAM as the theoretical model.
3.2 Variables in Previous Studies
Based on a survey of previous studies 15 important variables were identified and classified
into 4 groups: 5 variables which are included in the TAM; 7 variables related to the personal
characteristics of users; an attitudinal variable (Trust); and 2 variables related to
characteristics of the culture (Personal Relationships and Peer Influence).
TAM Variables: The definitions for each of the 5 TAM variables are displayed in
Table 3. These definitions were derived from those used in previous studies of PIB (see Table
2) and conform with the original definitions for the TAM given by Davis et al. (1989) except
for the definition of Actual Use which reflects that fact that the use of PIB is not mandatory
and the usual measures in terms of “how often?” and “how much?” have been replaced with a
measure of the proportion of banking transactions that are conducted by the individual using
PIB which represents the extent to which PIB is used (Goodhue and Thompson, 1995).
Table 3: Definitions of TAM Variables
TAM Variables
Perceived Usefulness
Perceived Ease of Use
Attitude
Intention
Actual Use
Definitions
The extent to which a person finds that using PIB enhances their banking activities.
The extent to which a person believes that using PIB is free of effort.
The individual user’s positive or negative feelings about using PIB.
The measure of the strength of an individual’s intention to use PIB.
The proportion of transactions conducted with PIB.
The TAM variables have been used in many previous studies even though those
studies were based on a different theoretical approach or were simply exploratory or
descriptive in nature. For example, Tan and Teo (2000) suggest that relative advantage is an
important factor in determining the adoption of innovations. PIB allows users to control their
accounts from almost anywhere at a convenient time with lower cost and this provides
relative advantages to the user in terms of price and convenience which are elements of the
perceived usefulness and ease of use of PIB (Polatoglu and Ekin, 2001). Also, Innovation and
Diffusion Theory proposes that the perceived usefulness of an innovation is positively related
to its rate of adoption (Rogers, 1983).
In some studies researchers have modified the TAM by removing Attitude and
retaining Intention because if the adoption of the technology is voluntary, as it is for PIB,
then these 2 variables are highly positively correlated (Venkatesh et al., 2003). Also, because
intentions generally result in actual use Intention has been removed in some studies and in the
study of PIB in Estonia by Eriksson et al. (2005) both of the variables Attitude and Intention
are missing in their final model.
Personal Characteristics: Studies have found that individual differences play a
crucial role in the implementation and acceptance of an innovation in a wide variety of
contexts including information systems, production, and marketing (Venkatesh and Morris,
2000). The 7 variables defined in Table 4 relate to personal characteristics of users which
have been shown to play an important role in studies of PIB (De Blasio, 2008; Heaney, 2007;
Hernandez and Mazzon, 2007; Rugimbana, 2007a, 2007b) In the context of the TAM they
are included as external variables with direct effects on Perceived Ease of Use, Perceived
Usefulness, and Attitude.
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Table 4: Personal Characteristics of Users in PIB Studies
Personal Characteristics
Gender
Age
Level of Education
Income
Internet Experience
Internet Banking Experience
Position Description
Definitions
Male or female.
Measured in years.
Highest level of award.
Monthly salary (Baht’000).
Number of months/years using the Internet.
Number of months/years using PIB.
The individual’s position in their organization in terms of their responsibilities
and their span of control.
Previous studies of PIB have established correlations among these variables. For
example, compared to younger users older users normally have: higher levels of education;
higher incomes; more Internet and PIB experience; and work in higher level positions in
terms of their responsibilities, involvement in decision making, and span of control. Findings
related to associations between gender and these variables have been different. For recent
studies conducted in developed nations and some developing nations there has been little
difference between males and females. However, there are findings in developing nations
which indicate that compared to males females are less likely to: have higher levels of
education; occupy senior level positions in organizations; receive higher level incomes; and
use the Internet and PIB. For example, in a study conducted in Brazil Hernandez and Mazzon
(2007) examined personal characteristics (age, income, level of education, and gender) and
found evidence to support the argument that younger males with a college degree and higher
income are more likely to adopt PIB.
In a study of PIB in Thailand Jaruwachirathanakul and Fink (2005) confirmed results
from previous studies (Pikkarainen et al., 2004; Mols, 2000) that the success of PIB is
determined not only by support from banks and government but is strongly influenced by the
customer’s personal characteristics. In their study gender, level of education, income, Internet
experience, PIB experience, but not age were found to have a statistically significant impact
on the factors that influenced the adoption of PIB.
Trust: Trust is referred to as an attitudinal variable and it is one of the most important
factors affecting the adoption and use of Internet based applications and PIB in particular and
all of the studies in Thailand (see Table 1) have found trust to be an important factor. Studies
have shown that customers do not trust PIB for 4 main reasons: security; the service
provider’s reputation; privacy; and risks associated with the reliability of the services and
they appear to be much more concerned with the security of PIB than they are with the
security of traditional banking. However, compared to non-users those who are already using
PIB seem to have more confidence that the system is trustworthy (Gerrard and Cunningham,
2003).
Many studies have shown that Trust has a direct effect on Perceived Usefulness,
Perceived Ease of Use, and Attitude and in a TAM study of PIB Suh and Han (2002) showed
that trust has a significant effect on Attitude, Intention, and Actual Use. In another TAM
study Eriksson et al. (2005) measured Trust with 2 valid indicators (security and privacy) and
found that Trust had a significant effect on Perceived Usefulness and Perceived Ease of Use.
In a study in Thailand Jaruwachirathanakul and Fink (2005) conceptualized Trust in terms of
risk and privacy and although it was found to be an important determinant of the adoption of
PIB it was not as important as features of the website which affect Perceived Usefulness and
Perceived Ease of Use. In this study Trust is defined in terms of the individual’s perception of:
the security of the system; the service provider’s reputation; loss of privacy; and concerns
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about risks associated with the reliability of PIB and it is proposed to have a direct effect on
Perceived Usefulness, Perceived Ease of Use, and Attitude.
Characteristics of Culture: Subjective norms describe social pressures that may
affect an individual’s intention to perform an action. In the context of PIB this concept has
been referred to in terms of 2 characteristics of the society’s culture: the value placed on
personal relationships; and the influence on the individual of the opinions of others. These 2
characteristics are prevalent in Thai culture where the importance of personal relationships
and the influence of those who are close to an individual are the cornerstones of Thai society
(Hofstede, 2008). PIB has the potential to reduce the opportunity to develop personal
relationships with banks through a reduction in face-to-face interactions and the influence of
peers may affect the individual’s adoption and use of PIB. Definitions for the variables
Personal Relationships and Peer Influence are presented in Table 5 together with a selection
of references to recent studies of PIB that have included both of these variables.
Table 5: Characteristics of Culture
Characteristics
of Culture
Personal
Relationships
Peer Influence
Definitions
Previous Studies
The individual’s perceptions of changes to personal Herington and Weaven (2007); Hernandez
relationships when dealing with the bank and the and Mazzon (2007); Kivijärvi et al. (2007);
lessening of face-to-face contact with bank personnel. Rugimbana (2007a, 2007b); Shun-Fong et
The influence on the individual due to the attitudes of al. (2007); Srivastava (2007);
Jaruwachirathanakul and Fink (2005)
friends and colleagues to PIB.
In this study, as in many previous TAM studies of PIB, Personal Relationships and
Peer Influence are included as external subjective norm variables with direct effects on
Attitude and Intention. However, in the study by Jaruwachirathanakul and Fink (2005) in
Thailand neither of these characteristics of culture emerged as important determinants of the
adoption of PIB.
4.
THEORETICAL MODEL
Based on the review of previous studies of PIB the theoretical model illustrated in Figure 1
was developed. In Figure 1 the 5 TAM variables are all endogenous variables each with at
least one cause. The other 10 variables extend the TAM and are exogenous variables with no
proposed causes but with a direct effect on at least two endogenous variables.
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Figure 1: Theoretical Model
Personal Characteristics
Age
Gender
Level of Education
Income
Internet Experience
Internet Banking
Experience
Position Description
Perceived
Usefulness
Attitude
Attitudinal Variable
Trust
Intention
Actual Use
[ 5 Endogenous Variables ]
Characteristics of Culture
Personal Relationships
Peer Influence
Perceived
Ease of Use
[10 Exogenous Variables ]
Table 6 shows the type and level of measurement for each variable and the indicators
associated with latent variables. References are included that were found useful as a source of
measuring instruments which were used wherever possible in order to improve the validity
and reliability of the measures.
Table 6: Measurement of the Model Variables
Type and
Indicators for
Level of Measurement
Latent Variables
TAM Variables
Perceived Usefulness
Latent variable with indicators measured on
pu1, pu2, pu3, pu4, pu5, pu6, pu7.
Perceived Ease of Use
Likert scales treated as interval scale measures. pe1, pe2, pe3, pe4, pe5, pe6.
Attitude
at1, at2, at3, at4.
in1, in2, in3.
Intention
Actual Use
Single interval scale
Instruments: Eriksson et al., 2005; Goodhue and Thompson, 1995
Attitudinal
Latent variable with indicators measured on
Trust
t1, t2, t3, t4.
Likert scales treated as interval scale measures.
Instruments: Liao and Cheung, 2008; Eriksson et al., 2005; Jaruwachirathanakul and Fink, 2005
Characteristics of Culture
Personal Relationships
Latent variable with indicators measured on
c1, c2, c3.
Likert scales treated as interval scale measures.
Peer Influence
Single interval scale
Instruments: Kivijärvi et al. (2007); Shun-Fong et al. (2007); Srivastava (2007); Jaruwachirathanakul and Fink
(2005)
Personal Characteristics
Gender
Nominal scale
Age
Measured on ordinal scales in the questionnaire but for analyses they are converted to
Level of Education
interval scale measures as notated on the final questionnaire in Appendix A2.
Income
Position Description
Internet Experience
Single ratio scales
Internet Banking Experience
Instruments: De Blasio, 2008; Heaney, 2007; Rugimbana, 2007a, 2007b
Variables
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5.
DATA PREPARATION AND PRELIMINARY ANALYSES
5.1
Data Preparation
The final questionnaire was distributed to 700 users of PIB and 660 were returned but 38
were removed because they included missing values for at least 1 of the questions. The data
from the 622 acceptable questionnaires was entered into an SPSS worksheet and 10 percent
were selected randomly and checked for data entry errors. No errors were detected and only 4
outliers occurred among the measures for Age all in the 61-65 years category. The
corresponding 4 questionnaires were removed from the sample and the final sample size of
618 satisfies the minimum sample size of 600 determined for the study.
The construct validity of the measures of the indicators for the latent variables was
examined using a sequence of Principal Components factor analyses where indicators were
removed if they did not have a factor loading of at least 0.4 on one and only one of the
components (Straub et al., 2004).The final analysis is shown in Appendix A3 (Table A1(a))
where all of the indicators loaded cleanly onto their corresponding constructs except for the
indicators for Perceived Usefulness which all had significant cross loadings of at least 0.8 on
both Perceived Ease of Use and Intention. Consequently, the variable Perceived Usefulness
was deleted from the theoretical model since its indicators did not have satisfactory construct
validity. In addition, the indicator c1 for Personal Relationships was deleted after testing the
internal consistency reliability of the indicators using Cronbach Alpha coefficients and their
interpretation as described by George and Mallery (2003). Otherwise, the reliability of all of
the indicators was at least acceptable and in some cases excellent (Appendix A3, Table
A1(b)).
With the changes described above a descriptive statistical analysis was done for all of
the model variables as shown in Appendix A4 (Table A2). It is noted that the magnitudes of
skewness and kurtosis for all of the variables are within the limits of 3 and 7, respectively,
and this validates the use of the method of maximum likelihood estimation in the subsequent
SEM analyses (Kline, 2005).
5.2 Profile of Respondents
Based on Appendix A4 (Table A3(a)) it is seen that: the respondents are mostly between 31
and 35 years of age with only a few older than 40 (12 percent) or less than 26 (14 percent); 64
percent work in clerical officer positions and among the remainder the majority have
positions that involve supervisory managerial responsibilities; 76 percent are employed in
private enterprises; most have a bachelor or higher degree qualification (77 percent) and less
than 9 percent have a lower level of education; 67 percent have a monthly income between 15
and 45 thousand baht with most in the range 15-25 thousand and 16 percent receive less than
15 thousand while only 1 percent have incomes greater than 85 thousand. The respondents
have significant Internet experience with 77 percent having more than 5 years of experience
and only 7 percent have less than 1 year of experience. However, their experience with PIB is
quite different: 82 percent have been using PIB for less than 2 years and 43 percent of this
group has less than 3 months experience; only 4 percent have more than 4 years PIB
experience. It is not surprising that as the age of respondents increases there are
corresponding increases in their level of education, the level of their work position, their
income, and their experience with the Internet and PIB.
When Gender is cross tabulated against the other variables (Appendix A4, Table
A3(b)) it is seen that on average: the males are 34 years of age which is about 2 years older
than the average age for females; males have more formal education than females with 51
males holding master degrees compared to only 36 for females; and males have an monthly
income of 36,000 baht which is 6,000 baht more than females and there are 5 times as many
males as females with monthly incomes exceeding 85,000 baht. Males have more Internet
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and PIB experience than females and there are more females than males with 6 months or less
experience in these areas. There is no apparent difference between males and females with
regard to the type of organization where respondents are employed but males are more likely
to be in positions such as CEO/President/Managing Director, Supervisory Officer, and
Professional positions while females are more likely to be employed as Clerical Officers.
5.3 Preliminary Statistical Analyses
T-tests were used with Levene's test for the equality of variances in order to identify
significant differences due to gender in the means of the model variables. Statistically
significant differences between males and females were found for only 6 exogenous variables
(Age, Level of Education, Income, Internet Experience, Internet Banking Experience,
Position Description) and in each case the mean of the distribution for males was
significantly greater than the mean of the distribution for females. There were no statistically
significant differences between the means for males and females for the other 3 exogenous
variables (Trust, Personal Relationships, and Peer Influence) or any of the 4 endogenous
variables (Perceived Ease of Use, Intention, Attitude, and Actual Use). Since it is not
plausible to propose that Gender is a cause for any of the exogenous variables it was
concluded that the variable Gender can be removed from the theoretical model.
The analysis of the relationships between the means for the categories of Position
Description and the other model variables was conducted using ANOVA when the equality
of variances was established with Levene’s test or otherwise the Brown-Forsythe test was
used. For those model variables where there were statistically significant differences among
the means post-hoc comparisons were done using the Dunnet T3 test. The only model
variables where there were statistically significant differences among the categories of
Position Description were Age, the indicator c2 for Personal Relationships, Level of
Education, Income, and Internet Experience all of which are exogenous variables and the
post-hoc comparisons indicated that Clerical Officers: are younger than Managers,
Professionals, and Owner/Partners; have less income than Supervisors, Professionals,
Managers, and Owner/Partners; together with Managers have less Internet Experience than
Owner/Partners; and like Managers are less concerned with the loss of personal relationships
in dealing with a bank than Owner/Partners. Clerical Officers have lower levels of
educational awards than Professionals and Owner/Partners. Since it is not plausible to
propose that Position Description is a cause for any of the exogenous variables it was
concluded that the variable Position Description can be removed from the theoretical model.
Appendix A4 (Table A4) shows the nature of the correlation coefficients used to
measure associations among all of the model variables excluding Perceived Usefulness,
Gender, and Position Description which have been removed from the theoretical model as
described above. The statistically significant correlations support the cause and effect
relationships remaining in the theoretical model but also indicate that there are additional
possible cause and effect relationships and these are considered in the subsequent
development and analysis of the model.
6.
MODEL DEVELOPMENT AND ANALYSIS
In this study the SEM analyses use maximum likelihood estimation implemented with Amos
5 computer software (Arbuckle 2003) following the procedures described by Kline (2005).
The measurement models are formulated as latent structured regression (LSR) models.
Schumaker and Lomax (1996) note that the alternative path analysis and partially latent
structured regression models are best suited to exploratory studies where there may not be
established theory to support the structural aspects of the model. In this study the structure is
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11
supported by existing theory and the LSR measurement model was seen as being more
appropriate.
As described above Perceived Usefulness, Gender, Position Description, and the
indicator c1 for Personal Relationships are removed from the theoretical model. These
changes result in the model in Figure 2 which also includes the direct effects determined in a
SEM analysis of the model. Table 7 shows the values of the range of fit statistics associated
with the model (Kline, 2005). The fit statistics indicate that the model fit is not satisfactory
and further modifications may result in a parsimonious model with improved fit statistics.
Figure 2: Modified Theoretical Model
Age
- .178* M
- .045 S
Attitude
.164* M
.412* M
Level of
Education
- .118* M
.010 S
Income
.028 S
- .119 * M
Internet
Experience
Intention
- .193* M
- .006 S
.441* M
.185* M
Internet
Banking
Experience
.234* M
.123* M
.271* M
Perceived
Ease of Use
.452* M
Trust
.098* S
Peer
Influence
Actual Use
- .306* M
.088* S
Personal
Relationships
In Figure 2: * indicates a standardized direct effect which is statistically significant at
a level of 0.05 or less; and S and M represent effects which are small and medium,
respectively (Kline, 2005).
Table 7: Model Fit Statistics
Model
Modified
Theoretical
Model
(Figure 2)
N
Nc
618 148
NC (χ2/df)
1316.43/259
= 5.083
RMR
GFI
AGFI
.426
.861
.811
NFI
IFI
CFI RMSEA
.850 .876 .875
.081
R2: PE (.315), AT (.354), IN (.375), AU (.195)
Note: R2 is the proportion of variance explained by the variables which affect these variables.
In Figure 2 the coefficients for 6 paths are small and these paths may be removed.
Also, from Appendix A4 (Table A4) there are statistically significant correlations which may
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12
be associated with cause and effect relationships that have not been included in the model in
Figure 2 and these suggest the inclusion of an additional 13 paths. Thus there are 19 possible
modifications that may be made to the model in Figure 2.The specification search facility in
Amos 5 was used to systematically assess the effect on the model fit statistics resulting from
the inclusion of every possible combination of these 19 modifications. Each of the 19 paths
was made optional and this resulted in a hierarchy of 524,288 (219) models to be analyzed.
When the specification search was conducted the model with the best fit statistics was
found to include the substructure shown in Figure 3 where the 2 independent variables
Attitude and Intention have a positive correlation with the dependent variable Actual Use,
they correlate positively with each other, but surprisingly Attitude has a negative effect on
Actual Use. This situation is not common and is referred to as negative suppression where
Attitude is a suppressor and it is more likely to occur in models with latent variables
(Maassen and Bakker, 2001).
Figure 3: Model Substructure
Attitude
Intention
p1 = 258* M
(r1 = .404*)
p2 = .261* M
(r2 = .406*)
p3 = - .255* M
(r3 = .126*)
Actual Use
Note: Standardized effects (pi) with their medium magnitudes (M) and correlation coefficients (ri) are
shown where * indicates statistical significance at a level of 0.05 or less.
When the suppressor (Attitude) and the other independent variable (Intention)
correlate substantially then one or both of them may be removed from the model for reasons
of parsimony provided that there is an acceptable rationale for the removal of a variable
(Maassen and Bakker, 2001). It was noted previously that in TAM studies researchers have
often removed Attitude or Intention and consequently 3 options were tested: the removal of
either one; and the removal of both. This was done by using the specification search facility
to test the hierarchy of models corresponding to these options and the model with the best
range of fit statistics was selected as the final model shown in Figure 4 where Attitude is
removed but Intention is retained. The corresponding fit statistics are quite reasonable and are
shown in Table 8.
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Figure 4: Final Model
Internet Banking
Experience
Intention
.385*** M
.189*** M
.241*** M
.107* M
Trust
.156*** M
Actual Use
.360*** M
.535*** L
- . 182*** M
Personal
Relationships
Perceived Ease
of Use
- .243*** M
.125* M
Note: (a) *** and * indicate statistically significant effects at 0.001 and 0.05 levels, respectively. (b)
M and L indicate medium and large effects, respectively.
Table 8: Fit Statistics for the Final Model
Model
N
Nc
Final Model
618
(Figure 4)
169
NC (χ2/df) RMR GFI AGFI
531.63/108
.062 .905 .865
= 4.923
2
R : PE (.361), IN (.477), AU (.399)
NFI
IFI
CFI
RMSEA
.921
.936
.936
.080
Note: R2 is the proportion of variance explained by the variables which affect these variables.
Table 9 shows the complete analysis of all of the effects in the final model. Labels for
the variables are shown in the table and are used to simplify the labeling of paths. Labels for
all variables and indicators are specified in Appendix A2 and are used throughout the
Appendix sections A3 and A4.
Table 9: Analysis of Effects in the Final Model
Causal
Variables
Type of Effect
Direct
Internet
Banking
Experience
(IBE)
Indirect
Total Indirect
Total
Direct
Perceived Ease of Use
(PE)
.146 *** (.241 M)
Endogenous Variables
Intention
(IN)
None
None
IBE-PE-IN
.066*** (.129 M)
None
.146*** (.241 M)
.341*** (.360 M)
.066*** (.129 M)
.066*** (.129 M)
.086 * (.107 M)
Actual Use
(AU)
.335*** (.385 M)
IBE-PE-AU
.026* (.030 S)
IBE-PE-IN-AU
.021*** (.025 S)
.047*** (.055 S)
.382*** (.440 M)
.212*** (.156 M)
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Causal
Variables
14
Type of Effect
Perceived Ease of Use
(PE)
Endogenous Variables
Intention
(IN)
Trust
(T)
Indirect
None
T-PE-IN
.155*** (.192 M)
None
.341***(.360 M)
- .295*** (- .243 M)
.155*** (.192 M)
.241*** (.299 M)
- .188 *** (- .182 M)
None
PR-PE-IN
- .134*** (- .130 M)
Total Indirect
Total
Direct
None
- .295*** (- .243 M)
None
- .134*** (- .130 M)
- . 322*** (- .312 M)
.455*** (.535 L)
Indirect
None
None
Total Indirect
Total
Direct
Indirect
Total Indirect
Total
None
None
None
None
None
None
None
.455*** (.535 L)
None
None
None
None
Total Indirect
Total
Direct
Personal
Relationships
(PR)
Perceived Ease
of Use (PE)
Intention
(IN)
Indirect
Actual Use
(AU)
T-PE-AU
.061* (.045 S)
T-IN-AU
.027* (.020 S)
T-PE-IN-AU
.049*** (.036 S)
.137*** (.101 M)
.350*** (.257 M)
None
PR-PE-AU
- .053* (- .030 S)
PR-IN-AU
- .060** (- .034 S)
PR-PE-IN-AU
- .043*** (- .025 S)
- .156*** (- .089 S)
- .156*** (- .089 S)
.180* (.125 M)
PE-IN-AU
.145*** (.101 M)
.145*** (.101 M)
.325*** (.226 M)
.319*** (.189 M)
None
None
.319*** (.189 M)
Note: (a) ***, **, and * indicate statistically significant effects at 0.001, 0.01, and 0.05 levels,
respectively. (b) a (α) indicates an unstandardized (standardized) effect. (c) S, M, and L indicate small,
medium, and large effects, respectively (Kline, 2005).
From Table 9 it is seen that the standardized indirect effect of Trust on Intention
through the mediator Perceived Ease of Use along the path labeled T-PE-IN (0.192) is greater
than the direct effect of Trust on Intention (0.107). This indicates that Perceived Ease of Use
acts as a significant mediator in the effect of Trust on Intention and this is the only significant
mediation effect in the final model.
7.
DISCUSSION
The findings derived from the analysis of the final model are discussed first followed by a
comparison of the findings with those from previous studies.
7.1
Interpretation of the Final Model
Table 10 is extracted from Table 9 and refers to the total effects in the final model all of
which are statistically significant at a level of 0.001 or less.
Table 10: Summary of the Total Effects in the Final Model
Causal Variables
Internet Banking
Experience
Trust
Personal Relationships
Perceived Ease of Use
Medium, positive,
only direct
Medium, positive,
only direct
Medium, negative,
only direct
Endogenous Variables
Intention
Medium, positive,
only indirect
Medium, positive, mainly
indirect
Medium, negative,
mainly direct
Actual Use
Medium, positive,
mainly direct
. Medium, positive,
mainly direct
Small, negative,
only indirect
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Perceived Ease
of Use
None
.Large, positive,
only direct
Intention
None
None
Medium, positive,
mainly direct
Medium, positive,
only direct
Internet Banking Experience measures the length of time an individual has been
using PIB and its largest effect is on Actual Use, which measures the proportion of the
individual’s banking transactions that are conducted using PIB. Its second largest effect is on
Perceived Ease of Use, its third largest effect is on Intention, and each of these 3 effects is
positive and at least mainly direct. Consequently, the longer an individual has been using PIB
the more likely they are to find it easy to use and the more motivated they are to continue to
use it to conduct a significant number of their transactions.
Trust has its largest effect on Perceived Ease of Use, its second largest effect is on
Intention to Use, and its third largest effect is on Actual Use and all of these effects are
positive. If an individual believes that the bank: ensures privacy; keeps their data securely;
has a good public reputation; and provides reliable services then it is more likely that the
individual considers PIB easy to use and is strongly motivated to continue to use PIB to
conduct a significant number of their transactions. In particular, Perceived Ease of Use has a
significant mediation effect in the relationship between Trust and Intention. Increasing an
individual’s trust in the system significantly increases both directly and indirectly their
intention to continue to use the system. However, the indirect effect caused by an increase in
the perception that the system is easy to use exceeds the direct effect.
Personal Relationships measures the importance an individual places on having
personal relationships with their banks. Personal Relationships has its largest effect on
Intention, its second largest effect on Perceived Ease of Use, and its third largest effect on
Actual Use and in each case the effect is negative. The more importance an individual places
on personal relationships the less attracted they are to the use of PIB. Although the total
effect of Personal Relationships on Actual Use is negative it is small and only indirect but it
is noted that as the value placed on personal relationships increases the negative effects on
Intention and Perceived Ease of Use are important because, as discussed below, Intention and
Perceived Ease of Use have significant positive effects on Actual Use and these desirable
effects will be dampened for individuals who believe that a loss of personal relationships is a
negative consequence of using PIB.
Perceived Ease of Use only has and effect on Intention and Actual Use and the first is
the larger of these 2 positive effects. As might be expected, the easier an individual finds it to
use PIB the more likely they are to be motivated to continue to use these services to conduct a
significant number of their transactions. The best way to increase an individual’s perceptions
of the ease of using PIB is to increase their trust in these services. Also, the longer an
individual has been using PIB the more likely they are to find it easy to use. However, if an
individual places a strong emphasis on personal relationships then they will be more likely to
regard PIB as difficult to use.
Intention only has an effect on Actual Use. The effect is positive and direct. This
suggests that an individual who is motivated to continue to use PIB will conduct a significant
number of their transactions using these services. The largest positive effect on Intention is
due to Perceived Ease of Use, the second largest effect is negative and is due to Personal
Relationships, the third largest effect is positive and due to Trust, and the fourth largest effect
is positive and is due to Internet Banking Experience. All of these effects are medium and at
least mainly direct, except for the large direct effect due to Perceived Ease of Use. To
increase an individual’s intention to use PIB the services must be made easy to use, and
trustworthy. As the individual begins to use the services they will be motivated to continue
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their use but if the individual continues to place a high value on personal relationships then
this will have a negative effect on their intention to use the services.
Actual Use: If the objective is to increase the proportion of an individual’s banking
transactions that are conducted with PIB then the most important action is to get them started
in using PIB since the longer an individual uses the services the larger will be the proportion
of their transactions that they conduct using PIB. Increasing the individuals trust in the
services is also very important and the services must be made easy to use. Individual’s who
place an emphasis on personal relationships will be more difficult to convert to extensive use
of PIB.
7.2 Comparisons with the Findings of Previous Studies
The findings involving: the 3 TAM variables (Perceived Ease of Use, Intention, and Actual
Use); Internet Banking Experience; Trust; and Personal Relationships support the findings of
many previous studies identified in the literature review. However, the significant mediation
effect of Perceived Ease of Use in the relationship between Trust and Intention has not been
reported previously. In the study conducted in Thailand by Jaruwachirathanakul and Fink
(2005) Personal Relationships was not found to be important but in this study it was found to
have important direct and indirect effects on Perceived Ease of Use and Intention and it
indirectly affects Actual Use.
Both of the endogenous TAM variables Perceived Usefulness and Attitude are not
included in the final model. Even though the questions used to measure the indicators for
Perceived Usefulness were derived from existing measuring instruments they did not have
satisfactory construct validity because they were not distinct from the measures for Perceived
Ease of Use and Intention. Attitude was excluded because of its negative suppressor effect.
Neither of these findings concerning Perceived Usefulness and Attitude has been reported in
previous studies although in TAM studies Attitude has often been removed, while Intention
has been retained, because when the adoption of the technology is voluntary, as it is for PIB,
these variables are usually highly positively correlated.
The theoretical model (Figure 1) includes 7 exogenous variables which are not in the
final model (Figure 4). These 7 variables (Gender, Age, Level of Education, Income, Internet
Experience, Position Description, and Peer Influence) are not in the final model because they
were shown not to be significant causes for any of the endogenous variables. In many
previous studies some of these variables were found to be at least moderately important and
their omission from the final model represents a set of different findings. For example, in the
study conducted in Thailand by Jaruwachirathanakul and Fink (2005) Age and Peer Influence
were found to be unimportant but Gender, Level of Education, Income, and Internet
Experience did have a statistically significant impact on the adoption of PIB. In another study
in Brazil Hernandez and Mazzon (2007) found evidence to suggest that younger males with a
college degree and higher income are more likely to adopt PIB.
Although these 7 exogenous variables are not included in the final model they have
significant associations with each other and the other 3 exogenous variables which are in the
final model and the interpretation of these associations, which are not causal relationships,
reveals important information. In studies conducted in developed nations and some
developing nations there have been few differences between males and females. However,
among studies conducted in developing nations there are findings supported by this study
which indicate that compared to males females are less likely to: have higher levels of
education; occupy senior level positions in organizations; receive higher level incomes; and
use the Internet and PIB. The findings confirm the results from other studies that as the age of
an individual increases so does their level of education, their income, and their Internet and
PIB experience and their opinions about PIB are more likely to be influenced by their peers.
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Also, those working in higher level positions in organizations tend to be older and have
higher educational qualifications and income. There is also support for other previous
findings that: as experience with PIB increases so does trust in PIB; and as the emphasis
placed on personal relationships with banks increases trust in PIB decreases and the influence
of colleagues and friends increases.
8.
CONCLUSIONS
One of the objectives of the study is to provide practical advice for those responsible for the
development and use of PIB. Although these practical implications are probably of most
interest to banking institutions the implications are also relevant to other organizations which
are involved in conducting financial transactions with individuals. Public and private sector
organizations may be able to develop new financial services which are of benefit to the
organization and the client if the client is a PIB user. For example, direct electronic transfer
of funds in order to pay accounts associated with utilities can shorten the sequence of
processes associated with alternative methods of payment. Table 11 summarizes practical
objectives and associated actions for increasing the use of PIB.
Table 11: A Summary of Practical Objectives and Associated Actions
Objective
Actions
1. Increase PIB experience.
2. Increase trust in PIB.
1
Increase the 3. Improve the perceived ease of using PIB (see actions for objective 3).
actual use of 4. Increase intention to use PIB (see actions for objective 2).
PIB
5. Decrease the negative influence on the adoption of PIB due to a dependency on having
personal relationships with the bank.
1. Improve the perceived ease of using PIB (see actions for objective 3).
2
2. Decrease the negative influence on the adoption of PIB due to a dependency on having
Increase the
personal relationships with the bank.
intention to
3. Increase trust in PIB.
use PIB
4. Increase PIB experience.
1. Increase trust in PIB.
3
Increase the 2. Decrease the negative influence on the adoption of PIB due to a dependency on having
perceived ease personal relationships with the bank.
of using PIB 3. Increase PIB experience.
The evidence is that once an individual becomes a user of PIB the extent of their use
of PIB will increase and this will be maintained and strengthened by a corresponding increase
in their perception of the ease of using the system and an increased motivation to continue to
use the system. Actions designed to attract new PIB users should pay particular attention to
younger people who may not have graduate level educational qualifications and high incomes.
These individuals are less likely to be influenced by any negative comments concerning PIB
made by their colleagues and friends. In particular, this profile identifies young females who
are students or are working in clerical positions.
In order to encourage existing and new customers to adopt PIB banks may consider
promoting the trial use of these services with incentives including no fees and possible
rewards. At the same time promotional campaigns should stress the convenience of using PIB
at any time and virtually at any place compared to the apparent disadvantages of conducting
transactions at a bank branch at restricted times where customers have to queue for service,
complete forms, and probably confront the problems of travel and car parking.
To increase trust in PIB is a very important objective since this will have desirable
effects on the user’s perception of the ease of using the system, their motivation to continue
to use the system, and the extent to which they conduct their transactions using the system.
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An individual’s trust increases as they continue to use PIB. Trust can be increased by paying
careful attention to the development and enhancement of privacy, security, reliability, and
ensuring that the bank has a good reputation. Security features are of the utmost importance
and in this regard nothing should be compromised. Generally, customers refrain from using
PIB because they are apprehensive about breaches of security and the privacy of their
information especially if there is a perception that their account balances may be tampered
with. Banks have to continuously improve their security features and keep up-to-date with the
latest enhancements and these features need to be explained to customers in a language that
can be understood by the general public.
The perception that PIB is easy to use is reinforced if a customer has a high degree of
trust in the services. Not surprisingly, the more a customer uses the services the easier they
find them to use and this is mainly due to their increased familiarity with the user interface
including the dialogue design and the interaction methods. Ensuring the ease of use of PIB in
turn has a desirable effect on the customer’s motivation to continue to use the services more
extensively for their transactions. Consequently, from an interface design perspective good
design principles should be used consistently within the PIB system and they should be
consistent with good designs evident at other sites on the Internet with which the customer is
familiar. Standard functions such as: account balance; funds transfer; bill payment; and
transaction reports should all be made available and banks should consider expanding these
functions to accommodate emerging customer needs including loan and other applications,
new financial products, and new and improved services.
Many customers value friendly personalized services provided by bank officers at a
branch site where they feel they are known personally. These interactions encourage
customers to return to the same physical location rather than perform their banking
transactions using PIB. It is important but difficult to decrease the negative effect on the
adoption of PIB due to a strong dependency on having personal relationships with the bank
and its officers and this negative effect is greatest among those who have a low level of trust
in PIB and have opinions about PIB that are strongly influenced by their colleagues and
friends. A feeling that valuable personal relationships are lost when using PIB leads to a low
level of motivation to use the services, a perception that the services are not easy to use, and
consequently a reduction in the extent to which the services are actually used. If the use of
PIB is to increase then banks will need to personalize the interactions that customers
experience at PIB sites. This may be possible by making available more detailed analyses of
the customer’s financial position and presenting personalized financial advice, financial
incentives, products, and opportunities based on an analysis of the individual’s financial
position. However, it is not certain that such measures would be very effective and some
customers will continue to conduct the majority of their transactions in the conventional
manner at an office of the bank where they feel very comfortable with face-to-face
personalized services.
There are some additional comments regarding matters that are not examined
explicitly in the study but are relevant to the adoption of PIB. In Thailand many banks are
expanding the number of their retail branches and this is particularly evident from the
increase in the number of new sub-branch offices. Table 12 summarizes the changes in the
number of bank branches over the years 2006 to 2008 presented in a report produced in 2008
by the Bank of Thailand.
Table 12: Increases in the Number of Bank Branches
Year
2006
Number of Bank Branches
1120
Percentage Increase
26 (2006 to 2007)
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2007
2008
19
1408
1710
21 (2007 to 2008)
From Table 13 it is seen that there has been a steady increase of at least 21 percent in
the number of bank branches over this period and this increased availability of branches is in
line with objectives to bring banking services closer and more conveniently to customers
especially since many of the new branches are in shopping malls and are open at extended
times. This trend is likely to reduce the incentive for customers to use PIB.
If customers are encouraged to make payments using cash rather than by electronic
means then this will discourage increased use of PIB. For example, although many retailers
offer discount incentives for customers to make electronic payments there are still a
considerable number of retailers who deal only in cash or even impose an additional charge
of 2-3 percent if a customer wishes to use some form of electronic payment.
Information and communication technology infrastructure and usage is not highly
developed outside densely populated urban areas in Thailand and this study involved subjects
who live and work in the metropolitan areas of Bangkok where Internet access is readily
available. The use of PIB by those living outside urban areas was not examined but it is very
likely that the level of use by individuals in those areas is very low and this represents a
challenge and an opportunity for banks that wish to extend the availability of PIB to a broader
geographical cross-section of the population.
Automatic Teller Machine (ATM) services are readily available and are used
extensively by customers. Many of these services are the same as those provided by PIB and
by face-to-face services at bank branches and although it is not suggested that the availability
of ATM services should be reduced it is important to recognize that these must be taken into
account in any attempt to increase the use of PIB. It seems appropriate for banks to consider
seriously offering different services with PIB that will attract users and distinguish it from
other forms of customer interaction.
There are limitations associated with the study which suggest the need for the study to
be repeated and for related studies to be undertaken. The sample was selected from
individuals who work and live in the metropolitan areas of Bangkok and did not include
individuals from other urban or rural areas of Thailand. No lower limit was set on the period
of time that an individual had been using PIB. Consequently, the study included many
respondents whose PIB experience was minimal and it is possible that different results may
be obtained if these individuals were studied separately. Most of the respondents were
employed in non government organizations. This may not be a real limitation on the results
but further studies may investigate whether there are differences in the adoption of PIB that
are dependent on the organizational sector where the individual is employed. The
measurement validity for Perceived Usefulness and the role of Attitude to Use as a negative
suppressor both need further examination. Further studies in Thailand may be conducted
which consider the possibility of including other variables and causal relationships in the
model and there is a strong need for cross cultural studies to be undertaken. Such studies
would be particularly useful for foreign banks operating in Thailand that are already offering
PIB in other nations. Finally, it is important for studies to be undertaken which are concerned
with the adoption of corporate Internet Banking. This requires a change in the target
population and the unit of analysis and the services offered to organizations are different and
more complex than those offered by PIB.
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9.
20
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APPENDICES
A1. SAMPLE SIZE
A minimum size (n) for a random sample from a target population of size N is given by
n=
z 2 NP (1 − P )
(Tryfos, 1996). P is the population proportion and the
d 2 ( N − 1) + z 2 P (1 − P )
estimate of P obtained from the sample lies in the interval (P – d, P + d) with probability 1 –
α, which is the confidence level for this interval; z is obtained from tables for the standard
normal random variable Z such that the probability that Z > z is α/2; and d is the precision of
the confidence interval. The values of α and d are determined by the researcher and in the
absence of any knowledge about the value of P in the population 0.5 is used in order to obtain
a conservative value for the sample size. Using N = 1.7 million, a confidence level of 95
percent (α = 0.05), and 4 percent precision (d = 0.04) the minimum sample is size 600 which
corresponds to a sampling ratio (n/N) of 0.04 percent.
A2. NOTATED QUESTIONNAIRE
The final version of the questionnaire has been abbreviated and notated to indicate: (a) the
conversions used to change ordinal categorical measures for Age, Level of Education,
Position Description, and Income to interval measures; and (b) the labels used for variables
and their indicators throughout the Appendix sections A3 and A4..
Section 1: Personal Information
1.
2.
3.
4.
5.
6.
Gender (G) □ Male (1) □ Female (2)
Age (A) □ 25 or less (23) □ 31-35 (33) □ 41-45 (43) □ 51-55 (53) □ 61-65 (63)
□ 26-30 (28) □ 36-40 (38) □ 46-50 (48) □ 56-60 (58) □ 66 or more (68)
Level of Education (E)
□ Secondary School or equivalent (12) □ Diploma (14) □ Bachelor Degree (16)
□ Master Degree (18) □ Doctoral Degree (22)
Income (I)
□ Less than 5,000 (3) □ 35,000-44,999 (40) □ 75,000-84,999 (80)
□ 5,000- 14,999 (10) □ 45,000-54,999 (50) □ 85,000- 94,999 (90)
□ 15,000- 24,999 (20) □ 55,000-64,999 (60) □ 95,000-104,999 (100)
□ 25,000-34,999 (30) □ 65,000-74,999 (70) □ 105,000 or more (110)
Type of Organization
□ Government □ Government Supported □ Private □ Education Institution (e.g. private/public
university) □ Non-profit organization (e.g. foundation, charitable organization)
Position Description (J)
□ Clerical/Officer (e.g. general staff, accountant) (1) □ Supervisory Officer (2)
□ Consultant (3) □ Professional (e.g. doctor, lawyer, accountant) (4) □ Manager/Head of
Department (5) □ Owner/Partner (6) □ CEO/President/Managing Director (7) □ Student
□ Currently Retired □ Currently Unemployed
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Section 2: Internet and Internet Banking Experience
1. How long have you been using Internet? (Internet Experience (IE))
1
Less than 3
months
2
3 to 6
months
3
6 months to 1
year
4
1 to 2
years
5
2 to 3
years
6
3 to 4
years
7
4 to 5
years
8
More than 5
years
2. How long have you been using Internet Banking? (Internet Banking Experience (IBE))
1
Less than 3
months
2
3 to 6
months
3
6 months to 1
year
4
1 to 2
years
5
2 to 3
years
6
3 to 4
years
7
4 to 5
years
8
More than 5
years
Section 3: Perceived Ease of Use (PE)
I find that:
pe1. Learning to use
Internet Banking is easy
for me
pe2. It is easy to get
Internet Banking to do
what I want to do
pe3. Internet Banking is
flexible to interact with
pe4. It is easy to become
skillful using Internet
Banking
pe5. Using Internet
Banking is clear and
understandable
pe6. Internet Banking is
easy to use
Strongly
Disagree
Disagree
Slightly
Disagree
Neutral
Slightly
Agree
Agree
Strongly
Agree
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Strongly
Disagree
Disagree
Slightly
Disagree
Neutral
Slightly
Agree
Agree
Strongly
Agree
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Section 4: Perceived Usefulness (PU)
Using Internet Banking:
pu1. Improves my
personal banking
performance
pu2. Enables me to
complete banking
transactions quickly
pu3. Is useful for my type
of banking transactions
pu4. Allows me to do my
banking at a convenient
time
pu5. Allows me to do my
banking at a convenient
place
pu6. Reduces the costs of
doing my personal
banking
pu7. Provides a wide
range of information with
only “one click”
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Section 5: Trust (T)
I think that for Internet
Banking services my bank:
t1. Keeps customer data
securely
t2. Is well known and has a
good reputation
t3. Ensures the privacy of its
customers
T4. Provides reliable Internet
Banking services
Strongly
Disagree
Disagree
Slightly
Disagree
Neutral
Slightly
Agree
Agree
Strongly
Agree
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Section 6: Cultural Characteristics (Personal Relationships (PR), Peer Influence (PI))
c1. (PR) Using Internet Banking may change the way in which you establish personal contact with
your bank. How does this affect you?
1
Very
Acceptable
2
Quite
3
Slightly
4
Neither
5
Slightly
6
Quite
7
Very
Unacceptable
c2. (PR) Using Internet Banking may cause you to have to give up personal relationships when
dealing with your bank. How does this affect you?
1
Very
Acceptable
2
Quite
3
Slightly
4
Neither
5
Slightly
6
Quite
7
Very
Unacceptable
c3. (PR) Using Internet Banking may lessen the opportunity to have face-to-face conversations and
contact with people at your bank. How does this affect you?
1
Very
Acceptable
2
Quite
3
Slightly
4
Neither
5
Slightly
6
Quite
7
Very
Unacceptable
PI. If you had only a few friends or colleagues who used Internet Banking then how would that
influence your decision to use Internet Banking?
Encouraging
1
Very
2
Quite
3
Slightly
4
Neither
5
Slightly
6
Quite
7
Very
Discouraging
Section 7: Attitude (AT)
All things considered, using Internet Banking for my personal banking needs is a ________ idea
at1.
Wise
7
Extremely
6
Quite
5
Slightly
4
Neither
3
Slightly
2
Quite
1
Extremely
Foolish
6
Quite
7
Extremely
Positive
at2.
Negative
1
Extremely
2
Quite
3
Slightly
4
Neither
5
Slightly
at3.
Harmful
1
Extremely
2
Quite
3
Slightly
4
Neither
5
Slightly
6
Quite
7
Extremely
Beneficial
at4.
Good
7
Extremely
6
Quite
5
Slightly
4
Neither
3
Slightly
2
Quite
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1
Extremely
Bad
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27
Section 8: Intention (IN)
I intend to use Internet
Banking:
in1. In preference to
visiting a bank branch
in2. Frequently in
conducting banking
transactions
in3. For quick and
convenient access to
banking information
Strongly
Disagree
Disagree
Slightly
Disagree
Neutral
Slightly
Agree
Agree
Strongly
Agree
1
2
3
4
5
6
7
1
2
3
4
5
6
7
1
2
3
4
5
6
7
Section 9: Actual Use (AU)
Considering all of your personal banking transactions what proportion of those transactions do you
conduct by using Internet Banking?
7
More than 95%
6
About 80%
5
About 65%
4
About 50%
3
About 35%
2
About 20%
1
Less than 5%
A3. PRINCIPAL COMPONENT FACTOR AND RELIABILITY ANALYSES
Table A1(a): Rotated component matrix (final PC analysis)
Components
AT
IN
PR
pe2
.141
.195
- .096
pe4
.140
.244
- .040
pe6
.114
.135
-.124
pe5
.075
.180
- .080
pe1
.179
.178
- .087
pe3
- .056
.141
-.108
t3
.195
.039
-.124
t1
.213
.088
- .084
t4
.071
.132
- .080
t2
.119
.216
- .043
at2
.796
.154
- .028
at3
.075
- .006
.769
at1
.118
-.135
.768
at4
.010
-.153
.760
in2
.087
- .098
.811
in3
.165
- .080
.781
in1
.302
-.151
.714
c3
-.143
-.111
.864
c2
-.109
-.120
.859
Rotation Sums of Squared Loadings
Component
Total
Percentage of Variance
PE
4.410
23.210
T
3.113
16.382
AT
2.748
14.461
IN
2.129
11.205
PR
1.649
8.677
Extraction Method: Principal Component Analysis. Rotation Method: Varimax, Kaiser Normalization.
Rotation converged in 5 iterations. Total Variance Explained: 74 percent
Indicators
PE
.837
.807
.806
.800
.800
.786
.190
.204
.245
.210
.100
.061
.102
.107
.282
.270
.309
-.137
-.163
T
.163
.179
.234
.236
.123
.137
.864
.859
.839
.742
.040
.140
.182
.153
.137
.181
.120
-.104
-.124
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Table A1(b): Cronbach alpha reliability coefficients
Variable
T
PE
IN
PR
AT
Indicators
t1, t2, t3, t4
pe1, pe2, pe3, pe4, pe5, pe6
in1, in2, in3
c2, c3
at1, at2, at3, at4
Cronbach’s Alpha
0.9028
0.9261
0.8270
0.7636
0.8145
Interpretation
Excellent
Excellent
Good
Acceptable
Good
Note: The reliability of the indicators for PR is improved from 0.7543 to 0.7636 by deleting c1.
A4. DESCRIPTIVE STATISTICS FOR MODEL VARIABLES
Table A2: Descriptive statistics for model variables
Variables/Indicators
Age (A)
Level of Education (E)
Income (I)
Internet Experience (IE)
Internet Banking Experience (IBE)
Position Description (J) (See note)
Peer Influence (PI)
Trust (T): t1
t2
t3
t4
Perceived Ease of Use (PE): pe1
pe2
pe3
pe4
pe5
pe6
Intention (IN): in1
in2
in3
Personal Relationships (PR): c2
c3
Attitude (AT): at1
at2
at3
at4
Actual Use (AU)
Minimum Maximum
23
12
3
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
2
1
2
1
58
18
110
8
8
7
7
7
7
7
7
7
7
7
7
7
7
7
7
9
7
7
7
7
7
7
7
Mean
32.85
16.01
30.87
7.28
2.88
1.94
4.39
4.12
4.66
4.11
4.50
4.86
4.83
4.68
4.85
4.70
4.74
5.28
5.01
5.28
3.13
3.28
4.71
4.44
4.52
4.54
2.61
Standard
Skewness Kurtosis
Deviation
6.741
0.523
0.069
1.237
-1.322
3.949
20.091
1.441
2.429
1.702
-2.628
5.967
1.818
0.734
-0.157
1.601
1.446
0.649
1.665
-0.006
-0.905
1.289
-0.200
-0.050
1.130
-0.251
0.094
1.251
0.042
0.068
1.263
-0.394
-0.005
1.344
-0.790
0.325
1.300
-0.720
0.012
1.257
-0.540
-0.171
1.226
-0.654
0.107
1.233
-0.458
-0.211
1.207
-0.443
-0.091
1.190
-0.905
1.026
1.246
-0.573
0.032
1.140
-0.657
0.523
1.159
0.257
-0.225
1.345
0.442
-0.319
1.006
0.545
-0.681
0.868
0.444
0.202
1.038
0.136
-0.323
0.925
0.514
-0.446
1.588
0.662
-0.528
Note: Three categories of Position Description (Student, Currently Retired, and Currently
Unemployed) were not included in the statistical analyses but they are included in the description of
the profile of the respondents (section 5.2). Consequently, for Position Description N = 578 (298
males, 280 females) but for all of the other variables N = 618 (318 males, 300 females).
The Electronic Journal on Information Systems in Developing Countries
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EJISDC (2009) 37, 6, 1-31
29
Table A3(a): Characteristics of respondents
Age
≤ 25
26 - 30
31 - 35
36 - 40
41 - 45
46 - 50
51 - 55
56 - 60
< 3 months
3 - 6 months
0.5 - 1 year
1 - 2 years
Frequency Percent
87
14.1
156
25.2
173
28.0
128
20.7
43
7.0
28
4.5
1
0.2
2
0.3
Internet Experience
21
3.4
10
1.6
11
1.8
12
1.9
2 - 3 years
10
1.6
3 - 4 years
4 - 5 years
28
4.5
48
7.8
> 5 years
478
77.3
PIB Experience
< 3 months
216
35.0
3 - 6 months
65
10.5
0.5 - 1 year
123
19.9
1 - 2 years
104
16.8
2 - 3 years
50
8.1
3 - 4 years
36
5.8
4 - 5 years
12
1.9
> 5 years
12
1.9
Level of Education
Secondary School
32
5.2
Diploma
21
3.4
Bachelor Degree
478
77.3
Master Degree
87
14.1
Cumulative
Cumulative
Income
Frequency Percent
Percent
Percent
14.1
< 5,000
25
4.0
4.0
39.3
5,000 - 14,999
75
12.1
16.2
67.3
15,000 - 24,999
198
32.0
48.2
88.0
25,000 - 34,999
139
22.5
70.7
95.0
35,000 - 44,999
76
12.3
83.0
99.5
45,000 - 54,999
22
3.6
86.6
99.7
55,000 - 64,999
34
5.5
92.1
100.0 65,000 - 74,999
26
4.2
96.3
75,000 - 84,999
11
1.8
98.1
3.4
85,000 - 94,999
3
0.5
98.5
5.0
95,000 - 104,000
2
0.3
98.9
6.8
≥ 105,000
7
1.1
100.0
8.7
Position Description
CEO/President/ Managing
10.4
3
0.5
0.5
Director
14.9
Owner/Partner
27
4.4
4.9
Manager/Head of
22.7
34
5.5
10.4
Department
100.0 Supervisory Officer
44
7.1
17.5
Clerical/Officer
397
64.2
81.7
35.0
Professional
66
10.7
92.4
45.5
Consultant
7
1.1
93.5
65.4
Student
29
4.7
98.2
82.2
Currently Retired
2
0.3
98.5
90.3
Currently Unemployed
9
1.5
100.0
96.1
Type of Organization
98.1
Government
39
6.3
6.3
100.0 Government Supported
45
7.3
13.6
Private
468
75.7
89.3
5.2
Educational
51
8.3
97.6
8.6
Non-Profit
4
0.6
98.2
85.9
Retired
2
0.3
98.5
100.0 Unemployed
9
1.5
100.0
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EJISDC (2009) 37, 6, 1-31
30
Table A3(b): Cross tabulations with gender
≤ 25
26 - 30
31 - 35
36 - 40
41 - 45
46 - 50
51 - 55
56 - 60
Variable
Age
Gender
M
F
36
51
74
82
92
81
73
55
27
16
14
14
0
1
2
0
Internet Experience
< 3 months
3 - 6 months
0.5 - 1 year
1 - 2 years
2 - 3 years
3 - 4 years
4 - 5 years
> 5 years
5
4
7
6
7
11
19
259
PIB Experience
< 3 months
3 - 6 months
0.5 - 1 year
1 - 2 years
2 - 3 years
3 - 4 years
4 - 5 years
> 5 years
93
36
62
65
30
19
4
9
Level of Education
Secondary School
Diploma
Bachelor Degree
Master Degree
8
10
249
51
Variable
Income
< 5,000
5,000 - 14,999
15,000 - 24,999
25,000 - 34,999
35,000 - 44,999
45,000 - 54,999
55,000 - 64,999
65,000 - 74,999
75,000 - 84,999
16 85,000 - 94,999
6 95,000 - 104,000
4 ≥ 105,000
6
Position Description
3 CEO/President/Managing Director
17 Owner/Partner
29 Manager/Head of Department
219 Supervisory Officer
Clerical/Officer
123 Professional
29 Consultant
61 Student
39 Currently Retired
20 Currently Unemployed
17
Type of Organization
8 Government
3 Government Supported
Private
24 Educational
11 Non-Profit
229 Retired
36 Unemployed
The Electronic Journal on Information Systems in Developing Countries
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Gender
M
F
8
17
28 47
85 113
76 63
42 34
12 10
30
4
20
6
7
4
2
1
2
0
6
1
3
0
14 13
19 15
28 16
186 211
43 23
5
2
17 12
2
0
1
8
12 27
29 16
247 221
27 24
0
4
2
0
1
8
EJISDC (2009) 37, 6, 1-31
31
Table A4: Sign and statistical significance of Pearson correlation coefficients (*indicates
statistical significance at 0.05 level or less (2-tailed))
E
I
IE
IBE
PI
t1
t2
t3
t4
c2
c3
pe
1
pe
2
pe
3
pe
4
pe
5
pe
6
in
1
in
2
in
3
at
1
at
2
at
3
at
4
AU
c3
pe
1
pe
2
pe
3
pe
4
pe
5
pe
6
in
1
in
2
in
3
at at at at
1 2 3 4
A
E
I
IE
IBE
PI
t1
t2
t3
t4
c2
+*
+*
+*
+*
+*
+
+
1
+*
+*
+*
+*
+
1
+*
+*
+*
+
+
1
+*
+*
+
1
+*
+*
+*
+*
+*
+
1
+
+
+*
+*
1
+*
+*
+*
-*
-*
1
+*
+*
-*
-*
1
+*
-*
-*
1
-*
-*
1
+*
1
-
+
+
+
+*
+
+*
+*
+*
+*
-*
-*
1
-
+
+
+
+*
+
+*
+*
+*
+*
-*
-*
+*
1
+
+
+
-
+*
-*
+*
+*
+*
+*
-*
-*
+*
+*
1
+
+
+
+
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
1
+
+
+
+
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
1
+
-
+
-
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
1
-
+
+
+
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
1
-
+
+
+
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
+*
1
-
+
-
-
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
+*
+*
1
-*
-
-*
-
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
+*
+*
+*
1
-*
+
-*
-
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
+*
+*
+*
+*
-*
+
-*
+
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+
+*
+*
+*
+*
+*
+*
+* +* 1
-*
+
-*
-
+*
-
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
+*
+*
+*
+* +* +* 1
+*
+*
+*
+*
+*
+
+*
+*
+*
+*
-*
-*
+*
+*
+*
+*
+*
+*
+*
+*
+*
+* +* +* +*
The Electronic Journal on Information Systems in Developing Countries
http://www.ejisdc.org
1