Comprehensive Framework For Internet Banking

Published Date: 02 Nov 2017

1. Introduction

Online banking (or Internet banking or E-banking) allows customers of a financial institution to conduct transactions of financial nature on a secure website operated by the institution. In simpler terms, it enables customers to gain electronic access to their accounts through the bank’s website, eliminating the intervention or inconvenience incurred by personally visiting the bank. Gone are the days, when one had to transact with a bank which was only in his local limits. Online banking has opened the doors for all customers, to operate beyond boundaries.

Internet Banking came to India with ICICI bank launching online banking services for its customers in 1996. Other banks followed suit, including HDFC, Citibank, IndusInd and more. Lower internet costs, rising awareness about the electronic medium, growing e-commerce and enhanced online security of sensitive information and transactions are today driving the penetration of online banking in everyday life. According to McKinsey India personal financial services survey 2011, "use of the Internet for banking has seen a massive rise in the 2010-11 survey, taking the overall number of bank consumers who use the Net to close 7% of the total bank account holders, a seven-fold jump since 2007, even as for the first time in the past 13 years, branch banking has come down by a full 15 percentage points during the same period".

A number of factors govern user acceptance of new technology, in particular, internet banking and researchers have tried to investigate what influences adoption of a particular technology by carrying out both theoretical and empirical studies. Many researchers have attempted to understand technology related consumer behaviour utilising the most commonly accepted and used Technology Acceptance Model (TAM) by Davis et.al. (1989) and a number of studies use the Technology Readiness (TR) Model by Parasuraman (2000). In addition, a number of studies try and integrate these two models often adding a number of demographic and psychographic parameters to study their effect and interplay. Yousafzai et al. (2012) have attempted to explain the role of TR and consumer demographics in the TAM model. Riquelme, H. and Rios, R. (2010) have tried to study the effect of gender on consumer acceptance in mobile banking. Grabner-Kräuter and Breitenecker (2011) have analysed factors affecting online banking adoption in Austria and have concluded Internet trust to be an individual difference variable predicting online banking adoption.

Modern statistical techniques of Structural Equation Modelling (SEM) are also been used recently to validate the causal relationships researchers propose in the area of technology adoption. Yi Yi Thaw et al. (2012) employ SEM to demonstrate consumer’s perspectives about e-commerce transactions. Abadi H., Ranjbarian B., Zade F (2012) have used the same technique to study mobile banking adoption.

The current study attempts to extend the TAM Model by drawing from these researches and presents an extended model for Online Banking Adoption. It then attempts to validate the model in Indian context by undertaking an empirical study using the structural equation modelling approach.

The paper is organized as follows: The first section presents a literature review of researches in the field on Online Banking Adoption studies. The research model and the hypothesis are elaborated in the next section. The instrument development and sample demographics are then described and finally the results of the Structural Equation Model hence developed are documented. The paper concludes with the discussion of the findings and the directions for future research with a stress on the limitations of the current study.

2. Literature Review

The surge of e-commerce and awareness amongst people about newer technology has led to institutions to launch online services and has driven researchers to focus their studies to understand and predict what drives a customer to use these services esp. in the latest fields of online banking, mobile banking, e-governance and e-retail. Ulun Akturan and Nuray Tezcan, (2012) have recently shown through a study at Galatasaray University through SEM approach how perceived risk and benefit influence the attitude of the youth towards adoption of mobile banking. Samar Mouakket, (2010) proposed a modified TAM model to explain adoption of e-governance in UAE. Gwang Jae Kim (2011), have applied the TAM model to the area of digital media broadcasting services.

In the field of online banking services in particular, Arpita Khare et al. (2012) have studied the influence of age and gender on technology adoption and have concluded that men and women are different in their attitude towards adoption of internet banking services and the young generation finds it more convenient to adopt online banking. The common thread amongst these studies is the Technology Acceptance Model (TAM) proposed by Davis (1989). TAM is the most widely used model in literature to describe and predict technology adoption by people. According to the TAM model, the two factors namely the perceived usefulness and the perceived ease of use, influence and predict a consumers attitude towards acceptance of the latest technology and further drives his behavioural intention to actually use the technology. (Davis, 1989; Venkatesh et al., 2003).

Perceived usefulness can be understood as the likeliness of a particular technology helping a person achieve enhanced performance in his job or activity (Davis, 1989). Rao et al. (2003) concluded that users will adopt a technology only if they feel benefit in doing so and believe that the technology will aid their performance at a job. The second factor proposed by the TAM model is the perceived ease of use which is according to Davis (1989) is the degree to which an adopter feels that the technology is a free effort while using it. Thus the more the potential users of a technology feel comfortable and at-ease about using and operating a technology, the more they are likely to adopt it. Lai and Li (2005) and Yaghoubi and Bahmani (2010) have used the TAM Model to explain adoption of internet banking as well as mobile banking services.

Another model that is vastly used to explain the technology adoption behaviour is the Technology Readiness model. Parasuraman (2000) coined the term "technology readiness" (TR), which refers to the propensity of a person to adopt and use new technology in his work or at home. Parasuraman defined TR as a comprehensive state of mind which is caused by the interplay of two kinds of factors – inhibitors and contributors. Together they describe a person’s inclination towards new technology. According to him, TR is composed of four components:

(1) Optimism;

(2) Innovativeness (both contributors);

(3) Discomfort; and

(4) Insecurity (both inhibitors).

Optimism and innovativeness are positive drivers of TR, encouraging customers to use technological products and services and to hold a positive attitude toward technology. Discomfort and insecurity are negative drivers, making customers reluctant to use technology. Parasuraman and Colby (2001) researched the target markets for technologically-based products and identified five categories of customers based on their technology readiness scores:

(1) explorers, who are a relatively easy group to attract when a new technology product or service is introduced and are the first to adopt technology because they have no fears about it (high on optimism and innovativeness and low on discomfort and insecurity);

(2) pioneers share the optimism and innovative views of explorers, but they also feel some discomfort and insecurity (high on optimism and innovativeness but above average on discomfort and insecurity);

(3) sceptics tend to be dispassionate about technology and also have some inhibitions (low on both optimism and innovativeness);

(4) paranoids may find technology interesting, but at the same time exhibit high degrees of discomfort and insecurity (high on optimism about technology but not very innovative); and

(5) laggards possess few motivations toward technology and typically would be the last group to adopt a new technological service or product (low on optimism and innovativeness and high on discomfort and insecurity).

Parasuraman and Colby (2001) found customer segments with differing TR profiles vary significantly in terms of Internet-related behaviours and indicated that not all users are equally ready to embrace technology-assisted services. Therefore, TR cannot be ignored in assessing customers’ adoption of technology-based services, and its role should be clarified and incorporated into any modelling of technology acceptance, especially in the context of SSTs (Lin and Hsieh, 2006; Verhoef et al., 2009). Very few studies have combined TAM and TR (e.g. Kleijnen et al., 2004; Walczuch et al., 2007, Lin et al., 2007). Even fewer studies have combined them in the context of banking services (Yousafzai et al., 2012).

Several factors are found to moderate attitude towards intention to adopt internet banking namely age, computer skills, technology readiness, and social influence (Kleijnen et al., 2004). Demographics are often used to segment customers (Dawar et al., 1992) and have been shown to influence perceptions of technology (Venkatesh and Morris, 2000), particularly online behaviour (Burke et al., 2002; Burroughs and Sabherwal, 2002; Moe, 2003) and IB (Branca, 2008). Thus, it is imperative to study the role these factors play in the relationship between beliefs and IB acceptance. Very Few studies have tried to consider the effects of demographics on technology acceptance (Yousafzai et al., 2011). The effect of gender has been studied by Hernan and Rios, 2010 and Yousafzai, 2011 however the influence of customer’s income group on the perceived usefulness and perceived ease of use has not been studied as per our knowledge. Moreover, analysis of age and gender in an Indian Context is still unexplored.

TAM’s fundamental constructs do not fully reflect the specific influences of external factors that may alter user acceptance (Moon and Kim, 2001; Wang et al., 2003). External factors often include individual differences, situational factors, and variables suggested from other theories (King and He, 2006). The basic model may be extended to study the effect of environmental or situational factors pertaining to a specific setting. Deng, Lu and Chen (2007); Li (2009); Deng, et al. (2010) have all used supplementing variables to the basic TAM model in the area of mobile banking adoption studies. Chong et al. (2010) have found trust and government support have an impact on the intention to use internet banking services by the Vietnamese. A similar study in Pakistan has concluded quality of the internet and security and privacy to be important variables influencing online banking adoption (Zahid et al. 2010). Another research has established trust and perceived risk to be crucial in explaining the internet banking usage intention (Zhao et al., 2009).

3. Research Model and Hypotheses

The study uses Technology Acceptance model (TAM) by Davis, 1989 as the base model. Thus the basic hypothesis as per TAM is:

H1: Perceived Usefulness influences Intention to use online banking

H2: Perceived Ease of Use influences Intention to use online banking

H3: Perceived ease of use positively influences Perceived Usefulness

Verhoef et al. (2009) called for an extension of Technology Readiness into existing technology adoption research in order to explore how it might influence the use of Self Service Technologies. While previous research has shown that consumer traits have a direct impact on technology use, recent studies suggest that personal traits may moderate the relationships between perceptions and technology use in the TAM (Dabholkar and Bagozzi, 2002; Yi et al., 2006). Kleijnen et al. (2004) found the positive relationship between PU and intention to use decreases as consumer’s exhibit technology readiness. This happens because if customers are more willing to embrace new technology, they do not care much of its relative advantage over other alternatives. Similarly a more technology ready customer would not mind about the ease of using the technology as they would believe in their own abilities to learn it. We thus propose the following hypothesis:

H4a: TR moderates the PU-intention to use link; Weaker for high TR score

H4b: TR moderates the PEU-intention to use link; Weaker for high TR score

A key limitation of the TAM is that while it provides a valuable insight into users’ acceptance and use of technology, it focus only on the determinants of use (PU and PEU) and does not reveal how such perceptions are formed or how they can be manipulated to foster users’ acceptance and increased usage (Mathieson, 1991). According to Davis et al. (1989), one of the key purposes of the TAM was to provide a basis for tracing the impact of external factors on internal beliefs, i.e. PU and PEU, and to link that to actual use.

The first external variable added to the TAM was output quality (Davis et al., 992), and since then researchers have proposed more than 70 external variables for PU and PEU. Yousafzai et al. (2007) in their meta-analysis of TAM have divided these external variables into four categories of organizational, system, users’ personal characteristics, and other variables. Of the user personal characteristics we have chosen age and gender and have tried to study their moderating influence on the PE and the PEU intention to use relationships. Thus, we propose the following hypothesis:

H5a: The PU-intention to use relationship is moderated by Age; PU drives intention to use for younger customers

H5b: The PEU-intention to use relationship is moderated by Age; PEU drives intention to use for older customers

H6a: The PU-intention to use relationship is moderated by Gender; PU is more relevant for males and influences their intention to use internet banking

H6b: The PEU-intention to use relationship is moderated by Gender; PEU is more relevant for females and influences their intention to use internet banking

Quality of Internet (Al-Somali et al., 2009) refers to the speed and reliability of internet connection and is crucial in determining consumer online acceptance. It is particularly significant in the Indian context as high speed and reliable internet is still a far-fetched vision in many areas and may be one reason why customers choose to visit a nearby branch instead of exercising internet banking. We propose Internet quality to be a predictor variable governing intention to adopt online banking.

H7: Internet Quality will positively impact intention to use online banking

Even with a good quality internet, people may not be driven to using internet for conducting financial transactions because of the perceived risk associated with it. Zhao et al. (2009) explained that the riskiness of transactions perceived by customers can be minimized to a great extent by enhancing the trust level of the customer in the bank providing the online services. Cheung and Lee, 2001, as cited by Lim, 2003 have also found trust to be a precedent of perceived risk. We thus try and study the impact of trust in one’s bank to deliver internet banking competence on internet banking usage and adoption.

H8: Trust in the bank to provide efficient internet banking services has a positive impact on a customer’s intention to use Online banking services.

All the above hypotheses and the demographic and psychographic integrated with the basic TAM model are shown in figure 1 as a comprehensive framework we propose for the study of online banking adoption.

Figure 1: A Comprehensive Internet Banking Acceptance Model (CIBAM)

4. Research Methodology

4.1 Initial Measures

The original scale for measuring Technology Readiness by Parasuraman (2000) is a 36 item scale. Lin and Hsieh (2011) in their paper titled "Refinement of the technology readiness index scale: A replication and cross-validation in the self-service technology context" have refined the scale to a 16 item scale and proved it to have sound psychometric properties based on findings from various reliability and validity tests, as well as scale replications employing several samples. The refined scale uses 5 items for Optimism, 4 each for Innovativeness and Discomfort and 3 for Insecurity. The original scale and the reduced scale items used in this study are indicated in Figure 2.

To measure the parameters of the TAM Model, constructs were adapted from literature and previous researches. Behavioural Intention was adapted from Ajzen and Fishbein (1980). The respondents were asked to evaluate their intention of using internet banking for the following five services.

(1) Checking Account information

(2) Bill payment;

(3) e-TDR, online drafts/ cheques and other services

(4) Transferring money; and

(5) Online shopping payment

The constructs for Perceived Usefulness and Perceived Ease of Use have been adapted as such from Davis et al. (1989). To measure Quality of Internet and Trust in the Bank, a 4 item each scale was incorporated from Widjana & Rachmat, 2009. A five point Likert scale was used to measure each of the final 35 item survey instrument, ranging from 1 for strongly disagree to 5 for strongly agree.

4.2 Sample

The survey was conducted online and was send to 200 people via emails. The target group was chosen to represent a fair mix of people across age groups and gender. 131 respondents replied back with completed and valid surveys giving an acceptable response rate of 65.5%. The survey audience comprised of a mix of MBA students of an esteemed University and working professionals from two corporate offices of Mumbai area. The demographic profile of the respondents is indicated in Figure 3.

5. Results and Findings

5.1 Cluster Analysis

Cluster Analysis was performed on TR dimensions of Optimism, Innovativeness, Discomfort and Insecurity. A hierarchal cluster analysis was performed and upon comparing consecutive agglomeration coefficients form the agglomeration schedule, a 3 cluster solution seemed appropriate for the data. A k-means run for 3 clusters then showed the presence of Innovators (31.3 %), Pioneers (33.6 %) and Laggards (35.1 %). Paranoids and Sceptics were absent from this data set. The cluster centroids for each of these clusters and their number of member cases are stated in Figure 4.

5.2 Confirmatory Factor Analysis

To establish validity of the constructs used in the survey, a confirmatory factor analysis was run using Amos software. The CMIN/DF for the model was 1.873 at p<0.001. Other fitness indices indicated a good fit with CFI=0.875, NFI = 0.772 and RMSEA = 0.082. A reliability test (Figure 5) conducted revealed composite reliability (CR) for each of the constructs were greater than 0.5. To test convergent validity (Figure 6), the CRs were compared to the Average Variance Extracted (AVE), and were found to be greater. All AVEs were greater than the standard norm of 0.5. To test Discriminant Validity, Maximum Shared Squared Variance (MSV), and Average Shared Squared Variance (ASV) were calculated and found to be greater than AVE. Furthermore, Cronbach’s alpha measures also provided strong evidences of reliability (Figure 7).

5.3 Test of Hypotheses: Main Effects

A test of main effects (without the effect of moderating effect of Gender, Age and TR profiles) was performed using Structural Equation Modelling on Amos (Analysis of Moment Structure) Software. The model was found to be a moderate fit with CMIN/df=3., Probability level = .000. Although CMIN/df is higher than 3 which can be attributed to a smaller sample size, yet fit indices of CFI = 0.816 and NFI = 0.712 indicate a just acceptable fit. PEU and PU were found to be significantly related to Behavioural Intention to Use Internet Banking validating Hypotheses H1, H2 and H8 (Figure 8). The Perceived Ease of Use also affects Perceived Usefulness of Internet Banking validating Hypothesis H3. Impact of Trust in the Bank on Intention to Use Internet Banking was not significant at p<0.001, however was significant at p<0.01, thus validating Hypothesis H8. However, Hypothesis H7 that Quality of Internet positively impacts use of Internet Banking as it was not found to be significant at p<0.001 and p<0.01.

5.4 Tests for Moderation Effect

A multi group moderation analysis was performed using SEM on Amos to test for the moderating effect of Gender, Age Group and TR Profiles on the PEU-Intention to Use Link and the PU-Intention to Use Link. The age group was divided into three classes: young (15-25 years), adult (25-45 years) and old (>46 years of age). The TR groups were based on the three classes of clusters identified by the cluster analysis namely innovators, pioneers and laggards. The validity of hypothesis can be tested either by the difference in Chi-square values for the Unconstrained and Path constrained models or by calculating probability of significance from the regression weights and the difference in the critical ratios for each pair of parameters. We use the method of differences in critical ratio as they are easier to calculate and are can be calculated in one go. The findings are presented in Figure 9. The results indicated that TR, Age and Gender do have a moderating impact on the PU-Intention to Use and the PEU-Intention to Use link thus validating Hypothesis H4a, H4b, H5a, H5b , H6a and H6b. In particular, The PU-Intention to use link was stronger for Innovators (0.87) and Laggards (0.651) while the PEU-Intention to Use link was stronger for Pioneers (0.84).

For the moderation effect of age, it was observed that young people tended to be more influenced by Perceived Usefulness of Technology (0.65) while the old by the Perceived ease of Use (0.84).

Males were found to have stronger PU-Intention (0.782) to Use link while Females had a stronger PEU-Intention to Use link (0.710). These findings are similar to past research (Clegg and Trayhurn, 2000; Venkatesh et al., 2003).

6. Discussions and Managerial Implications

Banking is an information intensive activity and relies heavily on information technology to deliver services that are reliable, convenient and expedient to its customers. With the constant challenge to innovate and spread their market share, most banks today are choosing to spend in the cyberspace as a delivery medium than in brick and mortar. Yet, despite the obvious benefits of online banking services, not all choose to adopt it. The factors governing adoption of internet banking services in a developing country like India were investigated in this study.

The study validates the core TAM hypothesis that perceived usefulness and perceived ease of use has an impact on the behavioural intention to use internet banking. Furthermore perceived ease of use impacts the perceived usefulness. There were three TR profiles observed namely the innovators, pioneers and the laggards. The absence of the remaining two profiles of sceptics and paranoids of the total 5 profiles described by Parasuraman and Colby (2001), can be attributed to the smaller sample size of the study and the fact that the respondents were mostly from B-schools and Corporates where internet banking has penetrated to a deeper level.

The comprehensive Internet Banking Acceptance model presented aims to extend TAM model to include the effects of environmental factors of Internet Quality and Consumer Trust in the Bank. Consumer Trust in the Bank was shown to positively impact a consumer’s intention to use online banking services while Internet Quality did not seem to have a significant impact in this particular study.

Consumer demographics of Age and Gender were shown to significantly impact the PU-Intention to use and the PEU-Intention to Use Link. The Perceived Usefulness is a stronger determinant of acceptance of technology for males and younger population. On the other hand, the Perceived Ease of Use is stronger determinant for females and the older population.

For TR profiles, innovators laid more emphasis on PU as the driving factor for technology acceptance than laggards for whom the driving factor is the Perceived ease of use. An explanation for this could be that innovators are the ones who are most likely to be up-to-date and comfortable with the use of a new technology and hence, the motive for them to adopt a technology would be its usefulness rather than its simplicity and ease of use.

Knowledge of such findings can be crucial for banks aiming to increase the penetration of internet banking amongst their customers. Managers need to understand their customers and the differences they present in their attitudes toward new technology adoption. Marketing programs for males and youngsters should thrive on the benefits and advantages of using internet banking, while those for females and the adults and old, should focus on explaining them how to use the technology making it easier for them to adopt it. Customer segmentation on the basis of Technological Readiness scores can also help managers tailor their programs for individual customer profiles.

In summary, customer targeting strategies adopted by banks must keep the following recommendations in mind

Attract customers by emphasizing on the usefulness of internet banking; convenience, cost and time saving benefits, etc.

Attract customers by providing ease of use through user-friendly websites

Target the right customers i.e. the ones most likely to adopt the technology namely the innovators and the pioneers

Devise personal marketing programs to convince the least likely adopters

Build customer’s confidence and trust in the bank by demonstrating the security measures deployed to secure their transactions

Communicate benefits in all marketing campaigns to the young and provide instructions and assistance to the older generation making them comfortable with the use of technology

Develop differential marketing strategies to tap the male and the female customers

7. Limitations and Future Research

The current study tried to extend TAM by incorporating effect of some of the many psychographic, demographic and environmental/situational variables. Further attempts could be made to cover the effect of factors not covered by this study. Demographic factors of language and region may be studied. Environmental factors such as reference group influence third party contact and internet security and privacy may also be explored. The study can also be expanded to a larger sample size to see the varying behaviour across all Technology Readiness profiles identified by Parasuraman and Colby (2001).

The comprehensive internet banking acceptance model presented here could be validated for regions other than Mumbai in India and may also be extended to incorporate several location-specific factors. The framework presented in the paper can be extended further by incorporating more latent variables such as perceived risk involved in transacting on the internet, personal factors such as income level and education, social norms impacting the intention to use as well as external variables such as availability of government and technical support.

There have been sufficient studies carried out in developed countries. Internet adoption in developing countries is an emerging field of interest not only to researchers but to institutions looking to expand their territories of business. The knowledge of predictors of internet banking adoption will enable financial institutions to target and tap potential customers thereby leveraging from the diffusion if such services.

Parasuraman (2000) : 36 Item TR Scale

TR: Optimism

* Technology gives me more control over my daily life

New-technology products are more convenient to use

* I like the idea of Internet banking as I am not limited to regular banking hours

I prefer to use the most advanced technology available

I like computer programmes that allow me to tailor things to fit my own needs

* Technology makes me efficient in my occupation

I find new technologies to be mentally stimulating

* Technology gives me more freedom of mobility

* Learning about new technology can be as rewarding as the technology itself

While using my computer for internet banking, I feel confident that my computer will follow through what I am instructing it to do

TR: Innovativeness

* People come to me for advice on new technologies

I learn more than others about the new technologies

* I am first among friends to acquire new technologies

* I usually work out new high-tech products without help from others

I keep up with the latest technological developments in my area of interest

I enjoy the challenge of figuring out high-tech gadgets

* I have few problems in making technology work for me

TR: Discomfort

* Technical support lines are not helpful as they don’t use simple terms

* Sometimes, i think that technology systems are not designed for use by ordinary people

* There is no such thing as a manual for a high-tech product or service that is written in plain language

* New technologies are often too complicated to be useful

When I buy a high-tech product or service, I prefer to have a basic model over one with a lot of extra features

It is embarrassing when i have trouble with a high-tech gadget while people are watching

There should be caution in replacing important people-tasks with technology, because new technologies are not reliable

Many new technologies have health and safety risks that are not discovered until after people have used them

New technologies make it too easy for governments and companies to spy on people

Technology always seems to fail at the worst time

TR: Insecurity

* It is not safe to give credit card number over the internet

* It is not safe to do any kind of financial business online

* I worry that information send over the internet will be seen by other people

I don’t feel confident doing business with a place that can only be reached online

Any business transaction that I do electronically should be confirmed later with something in writing

Whenever something gets automated, I need to check carefully that the machine or computer is not making mistakes

The human touch is very important when doing business with a company

When I call a business, I prefer to talk to a person rather than a machine

If I provide information over the internet, I can never be sure if it really gets to the right place

Items Marked with * form part of the refined scale developed by Lin and Hsieh (2011)

Figure 2: Original and Refined TR Scale

DEMOGRAPHIC PROFILE

Frequency

Per cent

Gender

Male

84

64%

Female

47

36%

Age

Young (15-25 Years)

59

45.0%

Adult (26-45 Years)

36

27.5%

Old (>46 Years)

36

27.5%

Figure 3: Demographic Profile

Final Cluster Centers

Cluster

1

2

3

Optimism

4.24

4.19

2.94

Innovativeness

3.09

3.31

1.67

Discomfort

2.27

2.9

3.45

Insecurity

2.08

3.12

3.83

No. Of Cases

41

44

46

Per cent

31.3 %

33.6 %

35.1 %

Figure 4: Cluster Analysis Output

CR

AVE

MSV

ASV

Trust_in_Bank

0.820

0.540

0.538

0.445

Intention_to_Use

0.932

0.820

0.764

0.538

Perceived_Ease_of_Use

0.900

0.693

0.690

0.550

Perceived_Usefulness

0.912

0.723

0.718

0.447

Optimism

0.916

0.686

0.677

0.510

Innovativeness

0.844

0.593

0.482

0.384

Discomfort

0.808

0.519

0.361

0.254

Insecurity

0.806

0.581

0.394

0.306

Quality_of_Internet

0.876

0.642

0.567

0.411

Figure 5: Composite Reliability and Variance Measures

Discriminant Validity: Inter Construct Correlations

Trust_in_Bank

Intention_to_Use

Perceived_Ease_of_Use

Perceived_Usefulness

Optimism

Innovativeness

Discomfort

Insecurity

Quality_of_Internet

Trust_in_Bank

0.735

Intention_to_Use

0.762

0.905

Perceived_Ease_of_Use

0.784

0.874

0.833

Perceived_Usefulness

0.685

0.866

0.723

0.850

Optimism

0.810

0.814

0.897

0.718

0.829

Innovativeness

0.532

0.622

0.694

0.623

0.661

0.770

Discomfort

-0.383

-0.558

-0.534

-0.466

-0.522

-0.601

0.720

Insecurity

-0.501

-0.628

-0.602

-0.589

-0.551

-0.527

0.500

0.763

Quality_of_Internet

0.748

0.673

0.753

0.604

0.656

0.675

-0.438

-0.512

0.801

Figure 6: Inter Construct Correlations

Constructs

Standardized Regression Weights

***(Significant at p<0.001)

Behavioural Intention to Use (0.930)

In the next two months I will continue to use internet banking to communicate with my bank

0.9

***

In the next two months I will continue to use internet banking for most of my banking needs

0.922

***

In the next two months I will continue to use internet banking

0.894

***

Perceived Ease of Use (0.901)

I believe that internet banking websites do not require a lot of mental effort

0.793

***

I believe that internet banking websites have clear and understandable procedures

0.777

***

I believe that it was easy for me to learn how to perform the transactions available on my internet banking website

0.860

***

I believe that it was easy for me to become confident in my use of the transactions on my internet banking website

0.895

***

Perceived Usefulness (0.911)

I believe that internet banking enables me to conduct banking transactions more quickly

0.767

***

I believe that internet banking enables me to make the best use of my time

0.888

***

I believe that internet banking enables me to manage my financial resources more effectively

0.851

***

I believe that internet banking presents great benefits to me

0.89

***

Optimism (0.913)

Technology gives me more control over my daily life

0.785

***

I like the idea of Internet banking as I am not limited to regular banking hours

0.903

***

Technology makes me efficient in my occupation

0.798

***

Technology gives me more freedom of mobility

0.749

***

Learning about new technology can be as rewarding as the technology itself

0.896

***

Innovativeness (0.821)

People come to me for advice on new technologies

0.379

***

I am first among friends to acquire new technologies

0.824

***

I usually work out new high-tech products without help from others

0.864

***

I have few problems in making technology work for me

0.897

***

Discomfort (0.794)

Technical support lines used in online banking are not helpful as they don’t use simple terms

0.678

***

Sometimes, i think that technology systems are not designed for use by ordinary people

0.795

***

There is no such thing as a manual for a high-tech product or service that is written in plain language

0.836

***

New technologies are often too complicated to be useful

0.534

***

Insecurity (0.804)

It is not safe to give credit card number over the internet

0.801

***

It is not safe to do any kind of financial business online

0.702

***

I worry that information sent over the internet will be seen by other people

0.781

***

Quality Of Internet (0.868)

My access to internet is easy

0.603

***

My internet connection is fast

0.816

***

My internet connection is stable

0.917

***

The internet guarantees that all transactions to the bank have been completed

0.836

***

Trust in the Bank (0.832)

I trust my bank’s internet banking site

0.763

***

The internet banking site keep customers best interest in mind

0.514

***

The internet banking site keeps its promises and commitments

0.746

***

I trust in the benefits of the decision of the internet banking site

0.87

***

(Values in Brackets are Cronbach's Alpha)

Figure 7: Reliability and Validity Analysis

Regression Weights

Estimate

S.E.

C.R.

P

PU

<---

PEU

.700

.081

8.615

***

INTENTION_TO_USE

<---

PU

.476

.078

6.107

***

INTENTION_TO_USE

<---

TRUST

.199

.073

2.727

**

INTENTION_TO_USE

<---

PEU

.460

.076

6.050

***

INTENTION_TO_USE

<---

INTERNET_QUALITY

.004

.061

.073

.942

*** P<0.001, ** P<0.01

Figure 8: Test for Main Effects

MODERATOR

LINK

Hypothesis

Group 1

Group 2

Z-score

Outcome

Technology Readiness

INTENTION_To_USE<--- PU

H4a

Innovators

Pioneers

-2.125**

Accepted

Innovators

Laggards

-3.163***

Pioneers

Laggards

-1.307

Technology Readiness

INTENTION_To_USE<--- PEU

H4b

Innovators

Pioneers

1.245

Accepted

Innovators

Laggards

2.984***

Pioneers

Laggards

1.831*

Age

INTENTION_To_USE<--- PU

H5a

Young

Adult

-3.487***

Accepted

Young

Old

-0.385

Adult

Old

2.722***

Age

INTENTION_To_USE<--- PEU

H5b

Young

Adult

1.876*

Accepted

Young

Old

-0.365

Adult

Old

-1.983**

Gender

INTENTION_To_USE<--- PU

H6a

Male

Female

-3.631***

Accepted

 

INTENTION_To_USE<--- PEU

H6b

Male

Female

3.666***

Accepted

Notes: *** p-value < 0.01; ** p-value < 0.05; * p-value < 0.10

z scores are calculated on the basis of regression weights of the two groups and differences in critical ratios

Figure 9: Test for Multi Group Moderation