Using the TAM model to explain how attitudes determine adoption of internet banking

Abstract

In the advent of the information era, information technology has developed rapidly and has become significant for every business, particularly the banking industry. Commercial banks are currently changing their traditional banking services and becoming increasingly dependent on electronics. With many customer-oriented features, banking services receive significant attention from customers. Due to the lack of experience on online consumer behavior, this research aimed to study consumer behavior towards Internet banking of public and private commercial bank in India. This study investigates how customers perceive and adopt internet banking (IB) in India. A theoretical model was developed based on the Technology Acceptance Model (TAM). Questionnaire was designed and used it to survey a randomly selected sample of customers of Internet Banking. Results show that although respondents strongly believe that using Internet Banking would benefit their daily life, many issues such as security concerns and technology anxieties and social factors reduce their self efficacies. Its findings provide useful information for bank management in formulating IB marketing strategies and will benefit the organization in exploiting it to bring about competitive advantage and being able to retain customers as well as attract potential ones. The results of this study also indicate that perceived usefulness (PU) and perceived ease of use (PEOU) are also the important factors which influence the use of internet banking by customers. In addition, Social factors have strong and positive influence on adoption of internet banking.

Full text

European Journal of Economics, Finance and Administrative Sciences
ISSN 1450-2275 Issue 36 (2011)
© EuroJournals, Inc. 2011
http://www.eurojournals.com
Using the TAM Model to Explain How Attitudes Determine
Adoption of Internet Banking
Manoranjan Dash
Corresponding Author, HOD, Systems & IT, Faculty of Management Studies, Siksha O Anusandhan
University Ghatikia , Kalinga Nagar, Bhubaneswar, India
Tel: 09439616806
E-mail: manoranjanibcs@gmail.com
Ayasa Kanta Mohanty
Asst.Professor, Faculty of Management Studies, Siksha O Anusandhan University
Ghatikia, Kalinga Nagar, Bhubaneswar, India
Tel : 9861334658
E-mail: ayasamohanty@rediffmail.com
Sanjib Pattnaik
Asst. Professor, Faculty of Management Studies, Siksha O Anusandhan University
Ghatikia, Kalinga Nagar, Bhubaneswar, India
Tel: 09437028081
Email:sanjib.p@ibcs.ac.in
Ram Chandra Mohapatra
Asst.Professor, Faculty of Management Studies, Siksha O Anusandhan University
Ghatikia, Kalinga Nagar, Bhubaneswar, India
Tel: 9583011072
Email: ramachandra@ibcs.ac.in
Dhruti Sundar Sahoo
Lecturer, Faculty of Management Studies, Siksha O Anusandhan University
Ghatikia , Kalinga Nagar, Bhubaneswar, India
Tel:9337360054
Email: fordhruti@gmail.com
Abstract
In the advent of the information era, information technology has developed rapidly
and has become significant for every business, particularly the banking industry.
Commercial banks are currently changing their traditional banking services and becoming
increasingly dependent on electronics. With many customer-oriented features, banking
services receive significant attention from customers. Due to the lack of experience on
online consumer behavior, this research aimed to study consumer behavior towards Internet
banking of public and private commercial bank in India. This study investigates how
customers perceive and adopt internet banking (IB) in India. A theoretical model was
developed based on the Technology Acceptance Model (TAM). Questionnaire was
designed and used it to survey a randomly selected sample of customers of Internet

51 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
Banking. Results show that although respondents strongly believe that using Internet
Banking would benefit their daily life, many issues such as security concerns and
technology anxieties and social factors reduce their self efficacies. Its findings provide
useful information for bank management in formulating IB marketing strategies and will
benefit the organization in exploiting it to bring about competitive advantage and being
able to retain customers as well as attract potential ones. The results of this study also
indicate that perceived usefulness (PU) and perceived ease of use (PEOU) are also the
important factors which influence the use of internet banking by customers. In addition,
Social factors have strong and positive influence on adoption of internet banking.
Keywords: Internet Banking, Social influence, Consumer Behaviour.
1. Introduction
At present, banks and financial institutions have introduced and provided electronic banking which
allow customers to do their transactions online. These include balance enquiry, money transfer,
application for credit card, loan payment and bill payment over the Internet. Therefore, it is a clear
trend that e-banking has become a new wave for the financial sector since it provides lower cost of
transaction, creates a new stream by attracting customers with easy access and unlimited The banking
industry has been significantly influenced by evolution of technology. The growing applications of
computerized networks to banking reduced the cost of transaction and increased the speed of service
substantially. The nature of financial intermediaries made banks improve their production technology
by focusing on distribution of products. In other words, the evolution of banking technology has been
mainly driven by changes in distribution channels as we see evidence from over-the counter (OTC),
automated-teller-machine (ATM), phone-banking, tele-banking, pc-banking and most recently internet
banking (IB). However, we lack studies on consumer behaviour relative to the vast amount of literature
on firms’ behaviour regarding technology adoption and market structure. The new banking technology
can also face excess inertia as bank customers are somewhat tied to old technologies. More
importantly, risk aversion plays an important role in determining the probability of Internet Banking
has always been a highly information intensive activity that relies heavily on information technology
(IT) to acquire, process, and deliver the information to all relevant users. Not only is IT critical in the
processing of information, it provides a way for the banks to differentiate their products and services.
Banks find that they have to constantly innovate and update to retain their demanding and discerning
customers and to provide convenient, reliable, and expedient services. Driven by the challenge to
expand and capture a larger share of the banking market, some banks invest in more bricks and mortar
to enlarge their geographical and market coverage. In the arouse of the internet revolution, electronic
commerce emerged and allowed businesses to interact more effectively with their customers and other
corporations. In this proliferated digital age, banking industry has been using this new communication
channel to reach its varieties of customers. In particular, industries that are information-oriented such
as banking services and securities trading sector are expected to experience the highest growths in e-
commerce (Ibrahimet al. 2006; Hughes, 2002). Undoubtedly, Internet banking has experienced
explosive growth and has transformed traditional practices in banking (Barwise and Farley, 2005;
Gonzalez et al., 2008; Lichtenstein and Williamson, 2006). The banking industry as declared
information privacy and security to be major obstacles in the development of consumer related
electronic commerce. Besides that, success of banking industry depends on the capabilities of
management to anticipate and react to such changes in the financial marketplace (Gan et al., 2006).
Meanwhile Internet banking also allows customer to have direct access to their financial information
and to undertake financial transactions with more convenient way (Rotchanakitumnuai and Speece,
2003). For the success of most banks, it has become paramount to attract existing bank customers to
adopt the options of internet banking. This creates huge cost savings by means of scale effects in bank
operations (Chen and Hitt, 2002). Rao and Prathima (2003) provided a theoretical analysis of internet

52 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
banking in India, and found that as compared to the banks abroad, Indian banks offering online
services still have a long way to go. For online banking to reach a critical mass, there has to be
sufficient number of users and the sufficient infrastructure in place. Various authors have found that
internet banking is fast becoming popular in India (Gupta, 1999; Pegu, 2000; Dasgupta, 2002).
However, it is still in its evolutionary stage. In India, comparatively less number of studies has been
conducted on the current status of internet banking and customer satisfaction compared to other
countries. Thus, there is a lot of scope for the research to present new ideas concerning internet
banking in India which may be useful to the Indian banking industry.
2. Internet Banking
Internet banking allows customers to perform a wide range of banking transactions electronically via
the bank’s Web site. When first introduced, Internet banking was used mainly as an information
presentation medium in which banks marketed their products and services on their Web sites. With the
development of asynchronous technologies and secured electronic transaction technologies, however,
more banks have come forward to use Internet banking both as a transactional as well as an
informational medium. As a result, registered Internet banking users can now perform common
banking transactions such as writing checks, paying bills, transferring funds, printing statements, and
inquiring about account balances. Internet banking has evolved into a “one stop service and
information unit” that promises great benefits to both banks and consumers. Internet banking services
are crucial for long-term survival of banks in the world of electronic commerce (Burnham 1996). The
market for Internet banking is forecast to grow sharply in the next few years, affecting the competitive
advantage enjoyed by traditional branch banks (Duclaux 1996; Liao et al. 1999). Indeed, it also was
estimated that financial institutions that failed to respond to the need for Internet banking services
would likely lose more than 10% of their customer base by the year 2000 (Orr 1998; Tower Group
1996). Internet banking would help banks present a potentially low cost alternative to brick and mortar
branch banking. Wang et al. (2003) claim that in the 1990s Internet banking technology was under-
utilised as business organisations used it only to market their products and services. Tan and Teo
(2000) note that the challenge to expand and maintain banking market share has influenced many
banks to invest more in making better use of the Internet. The emergence of Internet banking had made
many banks rethink their Information Technology (IT) strategies in competitive markets. It is
suggested that the banks that fail to respond to the emergence of Internet banking in the market are
likely to lose customers and that the cost of offering Internet banking services is less than the cost of
keeping branch banking.
3. Review Literature
The technology acceptance model (TAM), developed by Davis et al. (1989), is one of the most widely
used and influential models in the field of information systems, technology and services. It has been
fully validated to be powerful as a framework to predict user acceptance of new technology. TAM
extended the theory of reasoned action (TRA) (Fishbein and Ajzen, 1980) by introducing two belief
factors, perceived usefulness and perceived ease of use, which substitute for many of TRA’s attitude
measure. These two factors are postulated to determine an individual's intention to use a technology-
based system with intention to use playing the role of mediator of actual system use. Perceived ease of
use is also posited to have a direct impact on perceived usefulness. In general, TAM is able to explain
up to 40% of the variance in usage intentions and 30% in system usage (Meister and Compeau, 2002).
To increase the predictive power of TAM, it was suggested to consider the role of external variables
(Davis, 1993). Legris et al. (2003) also noted the critical importance of examining external variables,
since they are the ultimate drivers for the use of technology. In a variety of disciplines, external
variables that were used to explore the effect on technology usage are individual differences, such as
cognitive, personality, demographic, and situational variables. (Zumd, 1979). An abundance of related

53 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
studies on TAM also found a significant relationship between individual differences and technology
acceptance (Hubona and Kennick, 1996; Jackson et al., 1997; Agarwal and Prasad, 1999;
Venkatesh, 2000; Venkatesh and Morris, 2000; Burton-Jones and Hubona, 2006). The TAM tends
to predict user adoption of new technologies in positive perspective. However, customers will reduce
their usage or even refuse to use a technology if they subjectively expect that an injury or a loss likely
occurs while using the technology. The degrees of risk that consumers perceive and their risk tolerance
are attitudinal factors that affect their usage (Chan et al., 2004). Perceived risk has multi-dimensions,
including financial, performance, physical, psychological, social and time risks (Jacoby and Kaplan,
1972; Havlena and DeSarbo, 1990; Murray and Schlacter, 1990; Stone and Gronhaug, 1993). There is
very little research to compare the effect of social influence (SI) on technology usage behavior.
Critiques of TAM and related theories have also suggested that the model has strong limitations in
terms of SI. Legris reviewed 22 articles published from 1980 to 2001 that used TAM and concluded
that TAM was a useful model, but it lacks the variables associated with social change processes (Stam,
Stanton, Guzman, 2005). This criticism suggested that in absence of SI the explanatory power of TAM
is limited despite its statistical success of its regression models. SI can be defined as “the degree to
which an individual perceives that important others believe he or she should use the new system”. The
role of SI in technology acceptance issues are complex and subject to a wide range of contingent
influences. (Venkatesh & Morris, 2000). Social elements exerted profound effects on the performance
of employees with the introduction of new technology. New technology affects the social organization
of work, access to resources and organizational structures. The construct SI has been incorporated from
the model of TRA, Personal Computing Utilization (PCU) and from Innovation Diffusion Theory
(IDT) (Srite, 2006). As technology advances, new systems have been introduced and users find more
alternatives to use the technology. While users are inclined to use some specific technology, their
reference groups might influence by suggesting to choose a certain alternative. Hence, the dimension
of SI might be an important factor while conducting research on adoption of technology (Kim, Jhang &
Lee, 2006). The construct of SI had a similar impact in the TAM relationships as it did in TPB (Morris,
Venkatesh, Ackerman 2005). The foundation of Social Influence in TAM was originated from
subjective norm as described in TRA. Subjective norm was included as a direct determinant of
behavioral intention in TRA and then in TPB. The reasoning for a direct effect of SI on intention is that
people may choose to perform a behavior even if they are not favorable towards that behavior or its
consequences. If they believe one or more important referents think that they should use the computers,
they are sufficiently motivated to act in accordance with the referents. Taylor and Todd (1995)
examined a direct significant effect on intention to use. Davis, Bagozzi & Warshaw (1989) found that
SI had no significant effect on intention to use and therefore it has been omitted from the original
TAM, but need for additional research to investigate the impact of social influence over intention to
use was emphasized continuously. (Venkatesh, Morris, & Davis, 2003). SI had a positive effect on
intention to use IT only when system use is mandatory. They also found that SI had a positive direct
effect on PU. Angst and Agarwal (2004) observed the effect of SI by including a multiple process of
compliance, identification and internalization and found that individuals were influenced by SI while
using IT. Song and Kim (2006) observed that SI has a very little effect on technology usage behavior.
But he emphasized to include this construct in the TAM model. On the basis of above studies, it is
hypothesized that SI will affect the intention to use the internet banking.
Figure 1: Technology Acceptance Model
Source: Davis , 1993 p.476

54 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
Figure 1: Technology Acceptance Model - continued
H5
Security
Perceived Usefulness
Perceived Ease of
U
se
Attitude Towards
Use Behavioral
Intention to Use Internet
Banking
Usage
Social
Influence
H1
H2
Security
H3
Security
H4
Security
Source: Davis , 1993 p.476
(PROPOSED RESEARCH MODEL)
4. Research Objectives
1. To understand the behavior of customers’ regarding use of internet banking.
2. To assess the social influence on acceptance and use of internet banking.
5. Research Methodology and Analysis
A total of 295 responses were received. We collected data from customers who use internet banking.
The questionnaire developed for TAM by Davis (1989) - adapting the scales for Perceived Usefulness
and Perceived Ease of Use .We tested the structural model by means of Confirmatory Factor Analysis
(CFA). An exploratory factor analysis using SPSS was conducted on the survey data. A seven-point
likert scale ranging from (1) ’strongly disagree’ to (7)’strongly agree’ were used to assess responses.
The measurement models specify how hypothetical constructs are measured in terms of the observed
variables. Furthermore, the structural model specifies causal relationships among the latent variables. It
is employed to describe the causal effects and amount of unexplained variance (Anderson and Gerbing,
1982). Structural equation modeling (SEM) was applied to evaluate the strength of the hypothesized
relationships among the constructs in the theoretical model developed by this study. Basically, SEM is
a family of statistical techniques that incorporates and integrates factor analysis and path analysis. It
can be utilized to model multivariate casual relationships and to test multivariate hypotheses. SEM
model building consists of a two-stage process (Jöreskog, and Sörbom, 1993; Hoyle, 1995; Hair et al.,
1998; Maruyama, 1998), in which the measurement models are tested before testing the structural
model. Confirmatory factor analysis (CFA) is conducted to assess the reliability and validity of the
measurement model, whereas the structural model is analyzed to evaluate the strength of the
relationships among constructs hypothesized in the research model.
H1: Perceived Social Influence will positively influence the behavioral intention to Internet
banking.
H2: Customer’s perceived ease of use has a significant impact on his/her perceived usefulness
of Internet banking.
H3: Customer’s perceived usefulness has a positive impact on his/her attitude towards using
Internet banking.
H4: Customer’s perceived ease of use has a positive impact on his/her attitude towards using
Internet banking.
H5: Customer’s attitude towards using Internet banking has a significant impact on his/her
intention to use it.

55 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
Measurement Model Analysis
Both, the R² and the path coefficients indicate how well the model is performing. R² shows the
predictive power of the model, and the values should be interpreted in the same way as R² in a
regression analysis. Partial Least Squares (PLS), an implementation of structural equation modeling
(SEM) was used to test the model and analyse the factors that affect customers attitude towards
Internet banking acceptance. Moreover, this approach was chosen because of its ability to test causal
relationships between constructs with multiple measurement items (Jöreskog and Sörbom,1993).
Table: Results for the Research Path Tests
Research Path R2 Path coefficient (β) P-Value
SI BI 0.316 0.690 0.000***
PEOU PU 0.594 0.784 0.000***
PU ATT 0.694 0.792 0.000***
PEOU ATT 0.589 0.782 0.000***
ATT BI 0.669 0.876 0.000***
*** p< 0.0001
Social
Influence
(SI)
Perceived
Ease of Use
Perceived
Usefulness
Attitude
towards use Behavioural
Intention
(BI)
Internet
Banking
Usage
0.690
0.784
0.782
0.792 0.876
(Results of Structural Equation Model)
It was found that awareness of Internet banking services and its benefits explains 63% of the
variance in perceived usefulness (PU). The paths had positive effect, with path coefficient of 0.792.
Meaning, hypotheses 3 was supported. Perceived Social Influence have significant effects on
Behavioural Intention to Internet Banking (BI) and together explain 67% of the variance .These two
factors had positive path coefficients that hypotheses 4 and 5 were also supported. Perceived ease of
use (PEOU) and perceived usefulness (PU) influenced customer attitudes towards using Internet
banking, supporting hypotheses 3 and 4. Theses factors had positive path coefficients Attitudes
towards (ATT) use explain 73% of the variance in adoption intention (AI) with path coefficients of
0.876. As a result, hypothesis 5 was also supported.
6. Conclusions
The results support the view that Perceived Ease of Use and Social Influence are predicting variables,
affecting Perceived Usefulness and Attitude as intervening variables, and Intention to Use internet
banking as the dependent variable. Perceived Usefulness and Perceived Social Influence has a direct
effect on Intention, while Perceived Ease of Use has only an indirect impact. The results of hypotheses
testing provide satisfactory support for the extended TAM through the SEM analysis. The research
model was based on an extension of the technology acceptance model with incorporating constructs of
social influence The findings provide useful insight for bank management in developing appropriate
marketing strategies to meet customers demands, and further to retain and expand customer base.

56 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
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Table 1: Reliability Analysis
Constructs Items Loading Composite Reliability Cronbach’s alpha(α)
Social Influence(SI) SI 1 0.779
0.817 0.865
SI 2 0.654
SI 3 0.965
SI 4 0.871
Perceived Ease of use(PEOU) PEOU 1 0.818
0.912 0.876
PEOU 2 0.813
PEOU 3 0.798
PEOU 4 0.867
Perceived Useful Ness(PU) PU 1 0.982
0.945 0.887
PU 2 0.932
PU 3 0.894
PU 4 0.821
Attitude Towards Use(ATT) ATT 1 0.786
0.872 0.813
ATT 2 0.795
ATT 3 0.812
ATT 4 0.654
Behavioural Intention To Use(BI) BI 1 0.956 0.921 0.868
BI 2 0.978
BI 3 0.872
Table 2: Age of Respondents
Frequency Percent Valid Percent Cumulative Percent
Valid 24-35 134 45.4 45.4 45.4
36-45 91 30.8 30.8 76.3
46-55 51 17.3 17.3 93.6
56-65 19 6.4 6.4 100.0
Total 295 100.0 100.0
Table 3: Gender of Respondents
Frequency Percent Valid Percent Cumulative Percent
Valid Male 253 85.8 85.8 85.8
Female 42 14.2 14.2 100.0
Total 295 100.0 100.0
Table 4: Occupation of Respondents
Frequency Percent Valid Percent Cumulative Percent
Valid Bank Executives 40 13.6 13.6 13.6
IT Professionals 67 22.7 22.7 36.3
Medical 35 11.9 11.9 48.1
Senior Management 22 7.5 7.5 55.6
Legal 30 10.2 10.2 65.8
Unemployed 23 7.8 7.8 73.6
General Administration 26 8.8 8.8 82.4
Businessman 7 2.4 2.4 84.7

58 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
Table 4: Occupation of Respondents - continued
Customer services 31 10.5 10.5 95.3
Academicians 14 4.7 4.7 100.0
Total 295 100.0 100.0
Table 5: Use of Internet
Frequency Percent Valid Percent Cumulative Percent
Valid Occasionally 29 9.8 9.8 9.8
Fortnightly 25 8.5 8.5 18.3
Weekly 39 13.2 13.2 31.5
Daily 202 68.5 68.5 100.0
Total 295 100.0 100.0
Table 6: Use of Internet Banking
Frequency Percent Valid Percent Cumulative Percent
Valid Occasionally 26 8.8 8.8 8.8
Fortnightly 38 12.9 12.9 21.7
Weekly 105 35.6 35.6 57.3
Daily 126 42.7 42.7 100.0
Total 295 100.0 100.0
Table 7: Use of Internet Banking Services
Frequency Percent Valid Percent Cumulative Percent
Valid Balance Enquiry 99 33.6 33.6 33.6
Statement 56 19.0 19.0 52.5
Bill Payment 34 11.5 11.5 64.1
Funds Transfer 48 16.3 16.3 80.3
Cheque Book requests 6 2.0 2.0 82.4
Other 52 17.6 17.6 100.0
Total 295 100.0 100.0
Table 8: Reason of Using Internet Banking
Frequency Percent Valid Percent Cumulative Percent
Valid Convenience 80 27.1 27.1 27.1
24/7 60 20.3 20.3 47.5
Inexpensive 53 18.0 18.0 65.4
From anywhere 46 15.6 15.6 81.0
Saves Time 23 7.8 7.8 88.8
Other 33 11.2 11.2 100.0
Total 295 100.0 100.0
Table 9: Reason for not using Internet Banking
Frequency Percent Valid Percent Cumulative Percent
Valid Awareness 47 15.9 46.1 46.1
Need Internet 21 7.1 20.6 66.7
Computer Skill 24 8.1 23.5 90.2
Other 10 3.4 9.8 100.0
Total 102 34.6 100.0
Missing System 193 65.4
Total 295 100.0

59 European Journal of Economics, Finance And Administrative Sciences - Issue 36 (2011)
Table 10: Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .686(a) .316 .387 .32978
2 .727 (b) .467 .506 .31456
a. Predictors: (Constant), PS
b. Predictors: (Constant), PS, PEOU
Test Statistics:
Type of
Bank
Age of
Respondents
Marital
Status
Education of
Respondents
Gender Of
Customer
Occupation of
respondents
Chi-Square (a,b,c,d) 2.119 100.919 83.556 37.797 150.919 81.576
df 1 3 1 2 1 9
Asymp. Sig. .146 .000 .000 .000 .000 .000