the adoption and use of personal internet banking services in thailand

EJISDC (2009) 37, 6, 1-31

The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

THE ADOPTION AND USE OF PERSONAL INTERNET BANKING SERVICES IN THAILAND

Surapong Prompattanapakdee

Assumption University, Thailand [email protected]

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 Unit of Analysis Focus of the Study References

United States of America

Individuals Urban/rural mobile PIB Drummond (2008)

Small Business CIB Gehling et al. (2007) Individuals PIB Kolodinsky et al. (2004)

United Kingdom Individuals Bank staff and PIB Durkin (2007) Organizations CIB Shah et al. (2007); Shah and Siddiqui (2006)

United Kingdom and Republic of Ireland Individuals PIB Daniel (1999)

Finland Individuals PIB Kuisma et al. (2007) Finland and Portugal Individuals PIB Kivijärvi et al. (2007)

Denmark Organizations CIB Mols (2000)

Estonia Organizations CIB Eriksson et al. (2008)

Individuals PIB Eriksson and Nilsson (2007); Eriksson et al. (2005)

Ukraine Organizations CIB Jennex and Amoroso (2002) Italy Individuals Urban/rural PIB De Blasio (2008)

Greece Organizations CIB Anthanassopoulos and Labroukos (1999) North Cyprus Individuals PIB Jenkins (2007)

Turkey Individuals PIB Polatoglu and Elkin (2001) Spain Individuals PIB Casaló et al. (2007)

European Union Individuals PIB Guerrero et al. (2007) United Arab Emirates Individuals PIB Khanfar (2007)

Egypt Organizations CIB Kamel and Hassan (2003)

India Banking Organizations CIB and PIB Malhotra and Singh (2007)

Bangladesh Banking Organizations CIB and PIB Alam et al. (2007)

Pakistan Individuals PIB Qureshi et al. (2008)


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Country Unit of Analysis Focus of the Study References Bank Managers CIB and PIB Kaleem and Ahmad (2008)

Singapore Individuals PIB Gerrard and Cunningham (2003); Tan and Teo (2000)

Malaysia

Individuals Age factors (PIB) Amin (2007); Rugimbana (2007a, 2007b)

Individuals PIB Lallmahamood (2007); Shun-Fong et al. (2007); Ainin et al. (2005)

Organizations CIB Suganthi et al. (2001)

Thailand Organizations CIB Chiarakul et al. (2007); Rotchanakitumnuai

and Speece (2003, 2004); Larpsiri et al. (2002)

Individuals PIB Jaruwachirathanskul and Fink (2005); Ongkasuwan and Tantichattanon (2002)

Taiwan Individuals PIB Shih and Fang (2004)

Hong Kong Individuals PIB Yiu et al. (2007); Cheng et al. (2006); Chan and Lu (2004)

South Korea Individuals PIB Suh and Han (2002)

Australia Individuals PIB Herington and Weaven (2007); Kam and Riquelme (2007)

Individuals Age factors (PIB) Heaney (2007)

Brazil Individuals PIB Botelho (2007); Hernandez and Mazzon (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

Previous Studies

Innovation and Diffusion Theory CIB Eriksson et al. (2008)

Critical Success Factors CIB Shah et al. (2007); Shah and Siddiqui (2006) Social Contagion Theory CIB Shi et al. (2008) The Decomposed Theory of Planned Behavior PIB Jaruwachirathanakul and Fink (2005); Shih and Fang (2004)

Technology Acceptance Model (TAM)

CIB Kamel and Hassan (2003)

PIB

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)

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 Definitions Perceived Usefulness The extent to which a person finds that using PIB enhances their banking activities. Perceived Ease of Use The extent to which a person believes that using PIB is free of effort. Attitude The individual user’s positive or negative feelings about using PIB. Intention The measure of the strength of an individual’s intention to use PIB. Actual Use 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 Definitions Gender Male or female. Age Measured in years. Level of Education Highest level of award. Income Monthly salary (Baht’000). Internet Experience Number of months/years using the Internet. Internet Banking Experience Number of months/years using PIB.

Position Description 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 Definitions Previous Studies

Personal Relationships

The individual’s perceptions of changes to personal relationships when dealing with the bank and the lessening of face-to-face contact with bank personnel.

Herington and Weaven (2007); Hernandez and Mazzon (2007); Kivijärvi et al. (2007); Rugimbana (2007a, 2007b); Shun-Fong et al. (2007); Srivastava (2007); Jaruwachirathanakul and Fink (2005) Peer Influence The influence on the individual due to the attitudes of

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

Attitude

Perceived Ease of Use

Perceived Usefulness

Intention Actual Use

Personal Characteristics Age

GenderLevel of Education

IncomeInternet Experience

Internet Banking Experience

Position Description

Characteristics of CulturePersonal Relationships

Peer Influence

[ 5 Endogenous Variables ]Attitudinal VariableTrust

[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

Variables Type and Level of Measurement

Indicators for Latent Variables

TAM Variables Perceived Usefulness Perceived Ease of Use Attitude Intention Actual Use

Latent variable with indicators measured on Likert scales treated as interval scale measures.

pu1, pu2, pu3, pu4, pu5, pu6, pu7. pe1, pe2, pe3, pe4, pe5, pe6. at1, at2, at3, at4. in1, in2, in3.

Single interval scale Instruments: Eriksson et al., 2005; Goodhue and Thompson, 1995

Attitudinal

Trust Latent variable with indicators measured on Likert scales treated as interval scale measures. t1, t2, t3, t4.

Instruments: Liao and Cheung, 2008; Eriksson et al., 2005; Jaruwachirathanakul and Fink, 2005 Characteristics of Culture

Personal Relationships Peer Influence

Latent variable with indicators measured on Likert scales treated as interval scale measures. c1, c2, c3.

Single interval scale Instruments: Kivijärvi et al. (2007); Shun-Fong et al. (2007); Srivastava (2007); Jaruwachirathanakul and Fink (2005)

Personal Characteristics Gender Age Level of Education Income Position Description Internet Experience Internet Banking Experience

Nominal scale Measured on ordinal scales in the questionnaire but for analyses they are converted to interval scale measures as notated on the final questionnaire in Appendix A2.

Single ratio scales

Instruments: De Blasio, 2008; Heaney, 2007; Rugimbana, 2007a, 2007b


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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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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

Level of Education

Income

Internet Experience

Internet Banking

Experience

Trust


Attitude


Intention

Actual Use

- .178* M

- .118* M

Peer Influence

- .045 S

.164* M

.010 S

.028 S

- .119 * M

- .006 S

.185* M

.271* M.452* M

.098* S - .306* M

.123* M

.412* M

.441* M

- .193* M

.088* S

.234* M

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 N Nc NC (χ2/df) RMR GFI AGFI NFI IFI CFI RMSEA Modified

Theoretical Model

(Figure 2)

618 148

1316.43/259 = 5.083 .426 .861 .811 .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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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

Actual Use

p1 = 258* M (r1 = .404*)

p2 = .261* M (r2 = .406*)

p3 = - .255* M (r3 = .126*)

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


Trust


Intention


Actual Use .156*** M

.385*** M

.241*** M

.107* M

.360*** M

- . 182*** M

- .243*** M

.535*** L

.189*** 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 NC (χ2/df) RMR GFI AGFI NFI IFI CFI RMSEA

Final Model (Figure 4) 618 169

531.63/108 = 4.923 .062 .905 .865 .921 .936 .936 .080

R2: PE (.361), IN (.477), AU (.399) 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

Endogenous Variables Perceived Ease of Use

(PE) Intention

(IN) Actual Use

(AU)

Internet Banking

Experience (IBE)

Direct .146 *** (.241 M) None .335*** (.385 M)

Indirect None IBE-PE-IN

.066*** (.129 M)

IBE-PE-AU .026* (.030 S)

IBE-PE-IN-AU .021*** (.025 S)

Total Indirect None .066*** (.129 M) .047*** (.055 S) Total .146*** (.241 M) .066*** (.129 M) .382*** (.440 M)

Direct .341*** (.360 M) .086 * (.107 M) .212*** (.156 M)


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Causal Variables Type of Effect

Endogenous Variables Perceived Ease of Use

(PE) Intention

(IN) Actual Use

(AU) Trust (T)

Indirect None T-PE-IN

.155*** (.192 M)

T-PE-AU .061* (.045 S)

T-IN-AU .027* (.020 S) T-PE-IN-AU

.049*** (.036 S) Total Indirect None .155*** (.192 M) .137*** (.101 M)

Total .341***(.360 M) .241*** (.299 M) .350*** (.257 M)


(PR)

Direct - .295*** (- .243 M) - .188 *** (- .182 M) None

Indirect None PR-PE-IN

- .134*** (- .130 M)

PR-PE-AU - .053* (- .030 S)

PR-IN-AU - .060** (- .034 S)

PR-PE-IN-AU - .043*** (- .025 S)

Total Indirect None - .134*** (- .130 M) - .156*** (- .089 S) Total - .295*** (- .243 M) - . 322*** (- .312 M) - .156*** (- .089 S)

Perceived Ease of Use (PE)

Direct None .455*** (.535 L) .180* (.125 M)

Indirect None None PE-IN-AU .145*** (.101 M)

Total Indirect None None .145*** (.101 M) Total None .455*** (.535 L) .325*** (.226 M)

Intention (IN)

Direct None None .319*** (.189 M) Indirect None None None

Total Indirect None None None Total 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 Endogenous Variables Perceived Ease of Use Intention Actual Use


Medium, positive, only direct

Medium, positive, only indirect

Medium, positive, mainly direct

Trust Medium, positive, only direct

Medium, positive, mainly indirect

. Medium, positive, mainly direct

Personal Relationships Medium, negative, only direct

Medium, negative, mainly direct

Small, negative, only indirect


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Perceived Ease of Use None .Large, positive,

only direct Medium, positive,

mainly direct

Intention None None 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 the actual use of

PIB

1. Increase PIB experience. 2. Increase trust in PIB. 3. Improve the perceived ease of using PIB (see actions for objective 3). 4. Increase intention to use PIB (see actions for objective 2). 5. Decrease the negative influence on the adoption of PIB due to a dependency on having personal relationships with the bank.

2 Increase the intention to

use PIB

1. Improve the perceived ease of using PIB (see actions for objective 3). 2. Decrease the negative influence on the adoption of PIB due to a dependency on having personal relationships with the bank. 3. Increase trust in PIB. 4. Increase PIB experience.

3 Increase the

perceived ease of using PIB

1. Increase trust in PIB. 2. Decrease the negative influence on the adoption of PIB due to a dependency on having personal relationships with the bank. 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 Number of Bank Branches Percentage Increase 2006 1120 26 (2006 to 2007)


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2007 1408 21 (2007 to 2008) 2008 1710

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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APPENDICES A1. SAMPLE SIZE A minimum size (n) for a random sample from a target population of size N is given by

( )( ) ( )PPzNd

PNPzn−+−

−=

121212

(Tryfos, 1996). P is the population proportion and the

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. Gender (G) □ Male (1) □ Female (2) 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) 3. Level of Education (E)

□ Secondary School or equivalent (12) □ Diploma (14) □ Bachelor Degree (16) □ Master Degree (18) □ Doctoral Degree (22)

4. 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)

5. Type of Organization □ Government □ Government Supported □ Private □ Education Institution (e.g. private/public university) □ Non-profit organization (e.g. foundation, charitable organization)

6. 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 2 3 4 5 6 7 8 Less than 3

months 3 to 6

months 6 months to 1

year 1 to 2 years

2 to 3 years

3 to 4 years

4 to 5 years

More than 5 years

2. How long have you been using Internet Banking? (Internet Banking Experience (IBE))

1 2 3 4 5 6 7 8 Less than 3

months 3 to 6

months 6 months to 1

year 1 to 2 years

2 to 3 years

3 to 4 years

4 to 5 years

More than 5 years

Section 3: Perceived Ease of Use (PE) I find that: Strongly

Disagree Disagree Slightly Disagree Neutral Slightly

Agree Agree Strongly Agree

pe1. Learning to use Internet Banking is easy for me

1 2 3 4 5 6 7

pe2. It is easy to get Internet Banking to do what I want to do

1 2 3 4 5 6 7

pe3. Internet Banking is flexible to interact with 1 2 3 4 5 6 7

pe4. It is easy to become skillful using Internet Banking

1 2 3 4 5 6 7

pe5. Using Internet Banking is clear and understandable

1 2 3 4 5 6 7

pe6. Internet Banking is easy to use 1 2 3 4 5 6 7

Section 4: Perceived Usefulness (PU) Using Internet Banking: Strongly

Disagree Disagree Slightly Disagree Neutral Slightly

Agree Agree Strongly Agree

pu1. Improves my personal banking performance

1 2 3 4 5 6 7

pu2. Enables me to complete banking transactions quickly

1 2 3 4 5 6 7

pu3. Is useful for my type of banking transactions 1 2 3 4 5 6 7

pu4. Allows me to do my banking at a convenient time

1 2 3 4 5 6 7

pu5. Allows me to do my banking at a convenient place

1 2 3 4 5 6 7

pu6. Reduces the costs of doing my personal banking

1 2 3 4 5 6 7

pu7. Provides a wide range of information with only “one click”

1 2 3 4 5 6 7


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Section 5: Trust (T) I think that for Internet Banking services my bank:

Strongly Disagree Disagree Slightly

Disagree Neutral Slightly Agree Agree Strongly

Agree t1. Keeps customer data securely 1 2 3 4 5 6 7

t2. Is well known and has a good reputation 1 2 3 4 5 6 7

t3. Ensures the privacy of its customers 1 2 3 4 5 6 7

T4. Provides reliable Internet Banking services 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?

Acceptable 1 2 3 4 5 6 7 Unacceptable Very Quite Slightly Neither Slightly Quite Very 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?

Acceptable 1 2 3 4 5 6 7 Unacceptable Very Quite Slightly Neither Slightly Quite Very 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?

Acceptable 1 2 3 4 5 6 7 Unacceptable Very Quite Slightly Neither Slightly Quite Very 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 2 3 4 5 6 7 Discouraging Very Quite Slightly Neither Slightly Quite Very Section 7: Attitude (AT) All things considered, using Internet Banking for my personal banking needs is a ________ idea at1.

Wise 7 6 5 4 3 2 1 Foolish Extremely Quite Slightly Neither Slightly Quite Extremely at2.

Negative 1 2 3 4 5 6 7 Positive Extremely Quite Slightly Neither Slightly Quite Extremely at3.

Harmful 1 2 3 4 5 6 7 Beneficial Extremely Quite Slightly Neither Slightly Quite Extremely at4.

Good 7 6 5 4 3 2 1 Bad Extremely Quite Slightly Neither Slightly Quite Extremely


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Section 8: Intention (IN) I intend to use Internet Banking:

Strongly Disagree Disagree Slightly

Disagree Neutral Slightly Agree Agree Strongly

Agree in1. In preference to visiting a bank branch 1 2 3 4 5 6 7

in2. Frequently in conducting banking transactions

1 2 3 4 5 6 7

in3. For quick and convenient access to banking information

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 6 5 4 3 2 1 More than 95% About 80% About 65% About 50% About 35% About 20% Less than 5%

A3. PRINCIPAL COMPONENT FACTOR AND RELIABILITY ANALYSES Table A1(a): Rotated component matrix (final PC analysis)

Indicators Components PE T AT IN PR

pe2 pe4 pe6 pe5 pe1 pe3

.837

.807

.806

.800

.800

.786

.163

.179

.234

.236

.123

.137

.141

.140

.114

.075

.179 - .056

.195

.244

.135

.180

.178

.141

- .096 - .040 -.124 - .080 - .087 -.108

t3 t1 t4 t2

.190

.204

.245

.210

.864

.859

.839

.742

.195

.213

.071

.119

.039

.088

.132

.216

-.124 - .084 - .080 - .043

at2 at3 at1 at4

.100

.061

.102

.107

.040

.140

.182

.153

.796

.769

.768

.760

.154

.075

.118

.010

- .028 - .006 -.135 -.153

in2 in3 in1

.282

.270

.309

.137

.181

.120

.087

.165

.302

.811

.781

.714

- .098 - .080 -.151

c3 c2

-.137 -.163

-.104 -.124

-.143 -.109

-.111 -.120

.864

.859

Component Rotation Sums of Squared Loadings Total Percentage of Variance

PE T AT IN PR

4.410 3.113 2.748 2.129 1.649

23.210 16.382 14.461 11.205 8.677

Extraction Method: Principal Component Analysis. Rotation Method: Varimax, Kaiser Normalization. Rotation converged in 5 iterations. Total Variance Explained: 74 percent


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Table A1(b): Cronbach alpha reliability coefficients

Variable Indicators Cronbach’s Alpha Interpretation T t1, t2, t3, t4 0.9028 Excellent PE pe1, pe2, pe3, pe4, pe5, pe6 0.9261 Excellent IN in1, in2, in3 0.8270 Good PR c2, c3 0.7636 Acceptable AT at1, at2, at3, at4 0.8145 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 Minimum Maximum Mean Standard Deviation Skewness Kurtosis

Age (A) 23 58 32.85 6.741 0.523 0.069 Level of Education (E) 12 18 16.01 1.237 -1.322 3.949 Income (I) 3 110 30.87 20.091 1.441 2.429 Internet Experience (IE) 1 8 7.28 1.702 -2.628 5.967 Internet Banking Experience (IBE) 1 8 2.88 1.818 0.734 -0.157 Position Description (J) (See note) 1 7 1.94 1.601 1.446 0.649 Peer Influence (PI) 1 7 4.39 1.665 -0.006 -0.905 Trust (T): t1 t2 t3 t4

1 7 4.12 1.289 -0.200 -0.050 1 7 4.66 1.130 -0.251 0.094 1 7 4.11 1.251 0.042 0.068 1 7 4.50 1.263 -0.394 -0.005

Perceived Ease of Use (PE): pe1 pe2 pe3 pe4 pe5 pe6

1 7 4.86 1.344 -0.790 0.325 1 7 4.83 1.300 -0.720 0.012 1 7 4.68 1.257 -0.540 -0.171 1 7 4.85 1.226 -0.654 0.107 1 7 4.70 1.233 -0.458 -0.211 1 7 4.74 1.207 -0.443 -0.091

Intention (IN): in1 in2 in3

1 7 5.28 1.190 -0.905 1.026 1 7 5.01 1.246 -0.573 0.032 1 9 5.28 1.140 -0.657 0.523

Personal Relationships (PR): c2 c3

1 7 3.13 1.159 0.257 -0.225 1 7 3.28 1.345 0.442 -0.319

Attitude (AT): at1 at2 at3 at4

1 7 4.71 1.006 0.545 -0.681 2 7 4.44 0.868 0.444 0.202 1 7 4.52 1.038 0.136 -0.323 2 7 4.54 0.925 0.514 -0.446

Actual Use (AU) 1 7 2.61 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).


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Table A3(a): Characteristics of respondents

Age Frequency Percent Cumulative Percent Income Frequency Percent Cumulative

Percent ≤ 25 87 14.1 14.1 < 5,000 25 4.0 4.0 26 - 30 156 25.2 39.3 5,000 - 14,999 75 12.1 16.2 31 - 35 173 28.0 67.3 15,000 - 24,999 198 32.0 48.2 36 - 40 128 20.7 88.0 25,000 - 34,999 139 22.5 70.7 41 - 45 43 7.0 95.0 35,000 - 44,999 76 12.3 83.0 46 - 50 28 4.5 99.5 45,000 - 54,999 22 3.6 86.6 51 - 55 1 0.2 99.7 55,000 - 64,999 34 5.5 92.1 56 - 60 2 0.3 100.0 65,000 - 74,999 26 4.2 96.3

Internet Experience 75,000 - 84,999 11 1.8 98.1 < 3 months 21 3.4 3.4 85,000 - 94,999 3 0.5 98.5 3 - 6 months 10 1.6 5.0 95,000 - 104,000 2 0.3 98.9 0.5 - 1 year 11 1.8 6.8 ≥ 105,000 7 1.1 100.0 1 - 2 years 12 1.9 8.7 Position Description

2 - 3 years 10 1.6 10.4 CEO/President/ Managing Director 3 0.5 0.5

3 - 4 years 28 4.5 14.9 Owner/Partner 27 4.4 4.9 4 - 5 years 48 7.8 22.7 Manager/Head of

Department 34 5.5 10.4

> 5 years 478 77.3 100.0 Supervisory Officer 44 7.1 17.5 PIB Experience Clerical/Officer 397 64.2 81.7

< 3 months 216 35.0 35.0 Professional 66 10.7 92.4 3 - 6 months 65 10.5 45.5 Consultant 7 1.1 93.5 0.5 - 1 year 123 19.9 65.4 Student 29 4.7 98.2 1 - 2 years 104 16.8 82.2 Currently Retired 2 0.3 98.5 2 - 3 years 50 8.1 90.3 Currently Unemployed 9 1.5 100.0 3 - 4 years 36 5.8 96.1 Type of Organization 4 - 5 years 12 1.9 98.1 Government 39 6.3 6.3 > 5 years 12 1.9 100.0 Government Supported 45 7.3 13.6

Level of Education Private 468 75.7 89.3 Secondary School 32 5.2 5.2 Educational 51 8.3 97.6 Diploma 21 3.4 8.6 Non-Profit 4 0.6 98.2 Bachelor Degree 478 77.3 85.9 Retired 2 0.3 98.5 Master Degree 87 14.1 100.0 Unemployed 9 1.5 100.0


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Table A3(b): Cross tabulations with gender

Variable Gender Variable Gender Age M F Income M F

≤ 25 36 51 < 5,000 8 17 26 - 30 74 82 5,000 - 14,999 28 47 31 - 35 92 81 15,000 - 24,999 85 113 36 - 40 73 55 25,000 - 34,999 76 63 41 - 45 27 16 35,000 - 44,999 42 34 46 - 50 14 14 45,000 - 54,999 12 10 51 - 55 0 1 55,000 - 64,999 30 4 56 - 60 2 0 65,000 - 74,999 20 6

Internet Experience 75,000 - 84,999 7 4 < 3 months 5 16 85,000 - 94,999 2 1 3 - 6 months 4 6 95,000 - 104,000 2 0 0.5 - 1 year 7 4 ≥ 105,000 6 1 1 - 2 years 6 6 Position Description 2 - 3 years 7 3 CEO/President/Managing Director 3 0 3 - 4 years 11 17 Owner/Partner 14 13 4 - 5 years 19 29 Manager/Head of Department 19 15 > 5 years 259 219 Supervisory Officer 28 16

PIB Experience Clerical/Officer 186 211 < 3 months 93 123 Professional 43 23 3 - 6 months 36 29 Consultant 5 2 0.5 - 1 year 62 61 Student 17 12 1 - 2 years 65 39 Currently Retired 2 0 2 - 3 years 30 20 Currently Unemployed 1 8 3 - 4 years 19 17 Type of Organization 4 - 5 years 4 8 Government 12 27 > 5 years 9 3 Government Supported 29 16

Level of Education Private 247 221 Secondary School 8 24 Educational 27 24 Diploma 10 11 Non-Profit 0 4 Bachelor Degree 249 229 Retired 2 0 Master Degree 51 36 Unemployed 1 8


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Table A4: Sign and statistical significance of Pearson correlation coefficients (*indicates statistical significance at 0.05 level or less (2-tailed))

A 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

E +* 1

I +* +* 1 IE +* +* +* 1

IBE +* +* +* +* 1 PI +* +* +* +* +* 1 t1 - - - - +* + 1 t2 - - - - +* - +* 1 t3 - - - - +* - +* +* 1 t4 + - + - +* + +* +* +* 1 c2 - - - - - +* -* -* -* -* 1 c3 + + + + + +* -* -* -* -* +* 1 pe 1 - + + + +* + +* +* +* +* -* -* 1

pe 2 - + + + +* + +* +* +* +* -* -* +* 1

pe 3 + + + - +* -* +* +* +* +* -* -* +* +* 1

pe 4 + + + + +* + +* +* +* +* -* -* +* +* +* 1

pe 5 + + + + +* + +* +* +* +* -* -* +* +* +* +* 1

pe 6 + - + - +* + +* +* +* +* -* -* +* +* +* +* +* 1

in 1 - + + + +* + +* +* +* +* -* -* +* +* +* +* +* +* 1

in 2 - + + + + * + +* +* +* +* - * - * +* +* +* +* +* +* +* 1

in 3 - + - - +* + +* +* +* +* -* -* +* +* +* +* +* +* +* +* 1

at 1 -* - -* - +* + +* +* +* +* -* -* +* +* +* +* +* +* +* +* +* 1

at 2 -* + -* - +* + +* +* +* +* -* -* +* +* +* +* +* +* +* +* +* +* 1

at 3 -* + -* + +* + +* +* +* +* -* -* +* +* + +* +* +* +* +* +* +* +* 1

at 4 -* + -* - +* - +* +* +* +* -* -* +* +* +* +* +* +* +* +* +* +* +* +* 1

AU +* +* +* +* +* + +* +* +* +* -* -* +* +* +* +* +* +* +* +* +* +* +* +* +*


the adoption and use of personal internet banking services in thailand

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