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Journal of Applied Sciences Research, 8(3): 1842-1852, 2012
ISSN 1819-544X
This is a refereed journal and all articles are professionally screened and reviewed
ORIGINAL ARTICLES
Corresponding Author: Hosein Khanifar, Associate Professor of Faculty of Management of Tehran University, Qom
College, Iran.
Tel: +989363076109 E-mail: ze_molavi@yahoo.com
Factors influencing the intendancy of e-banking: An integration of TAM & TPB with e-
service quality
1
Hosein Khanifar,
2
PhD, Zeinab Molavi,
3
MS, Gholam Reza Jandaghi,
4
PhD Mohammad Javad
Mohaghegh Niya, PhD
1
Associate Professor of Faculty of Management of Tehran University, Qom College, Iran
2
Master of science educated of Business- Financial Management, Tehran University, Qom College, Iran
3
Professor of Faculty of Management of Tehran University, Qom College, Iran
4
Assistant Professor of Faculty of Management of Allameh Tabtabai University, Tehran, Iran
ABSTRACT
In recent decades, banks have rapidly moved toward investment on e-banking systems to serve customers.
Despite of such investments, reports show that these technologies are not competently welcomed by customers.
Therefore, present study aims to identify affecting factors on customers’ intention to use e-banking. In present
study, electronic service quality integrated with Technology Acceptance Model and Theory of Planned Behavior
model to explain customers’ intention to use e-banking. Research statistical population consists of customers of
Passargad and Meli Banks in Qom Province that have used at least one of the e-banking systems so far.
Sampling method for Bank Meli is cluster method. Simple random method is used for Passargad Bank. Data
collection instrument is questionnaire. Totally, 353 filled questionnaires were returned. Structural Equation
Modeling was used to analyze the data. The results of structural equations modeling confirm model fitness in
studied population. Likewise, the results from path analysis show that perceived e-service quality and subjective
norm have significantly direct influences on intention to use. However, the findings indicate that perceived
behavior control and e-service quality have significantly indirect influence on intention to use. In the meantime,
shown other variables of models have non-significant influence on intention to use.
Key words: Technology acceptance model (TAM), Theory of planned behavior (TPB), E- banking, Electronic
service quality.
Introduction:
Over the past two decades, the banking industry has invested substantial resources in bringing information
technology to consumers. Responding to deregulation, rapid global networking, and rising income levels, the
banking industry has implemented new technology-based services called “e-banking” in order to achieve and
maintain strategic advantages (Joseph & Stone, 2003, p.190).
Although, in recent decade, in Iran, banks attempted to establish e-banking systems (Fatemi Ardecani,
2005, p.67); reports show that these technologies are not competently welcomed by customers (Moghli, 2007,
p.82).
Because of understanding and creating the conditions under which information systems will be embraced
by the individual remains a high-priority research issue(Venkatesh & Davis, 2000, p.186); many studies has
been conducted over the last decade in explaining and predicting user acceptance of information technology.
Although several research projects have focused on the factors that impact on the adoption of information
technology or internet for the past decade(Lee, 2009, p.130), there is limited empirical work which captures the
success factors of e-banking in Iran(such as Shahroodi & Sayad Azari,2010; Bagheri et al., 2009; Sayed Javadin
& Yazdani, 2005; etc) and in the other countries (such as Weerasekara,2011; Eze et al,2011; Shih & Fang,2004;
Polatoglu & Ekin,2001; etc).
On the other hand, the recent technological revolution has changed the perceived service quality of
customers and values to access lots of services. The Information Technology (IT) is being used extensively in
almost every field of life including banking sector (Rasheed & Latif, 2011, p. 59). Therefore, nowadays, the
mission of banks is delivery of qualified and suitable services to the customers (Hasani et al., 2008, p.11).
Because Internet is still a new transaction channel banks do not exactly understand what service is desired
from customers (Yang et al., 2004, p.1150). However, there were some studies focusing on retaining customers
on electronic environment, but few of them started from issues of service quality (Dai et al., 2005, p.1375).
Unfortunately, few studies have been done on service quality within e-banking (Aronsohn, 2006, p.25) and
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J. Appl. Sci. Res., 8(3): 1842-1852, 2012
likewise, no study has been done on service quality as a factor of adoption of e-banking. Therefore, as a basis of
promotion e-service quality, the firms should be founded understanding of how customers perceive and evaluate
e-service quality (Zeithaml, 2002, p.135). Thus, various dimensions and scales were developed to assess service
quality in electronic fields. In this study Parasuraman et al. (2005) basic scales were used to assess e-service
quality.
Previous researches review shows that TAM and TPB have been used in many studies to predict factors
influence user adoption an online system (Cheng et al., 2006; Lee, 2009; Wu and Chen, 2005); therefore, these
models are appropriate tools for understanding intendancy of e-banking use. This study integrates e-service
quality with the TAM and TPB model to explain customers’ intention to use e-banking. This study aims to:
1. To identify which factors are influencing the intention to use e-banking base on variables of TAM & TBP
model.
2. To investigate the significantly impact of perceived e-service quality on customers’ intention to use e-
banking.
This paper proceeds as follows: The literature on e-service quality will be introduced; outlines research model
and hypotheses; the methodology, data analysis, and results are detailed; and provides implications. Finally,
conclusions for further research and limitations of this research are addressed.
Literature review:
Electronic service quality:
In order to define the concept of e-service quality, at first, need to introduce the concept of service quality:
De Ruyter et al. (2001, p. 186) describe e-service as “content-centered and internet-based customer service,
driven by the customer . . . with the goal of strengthening customer-service provider relationships”.The concept
of e-service quality can be defined as “the customer overall evaluation and judgment of the excellence and
quality of e-service offering in the virtual marketplace” (Santos, 2003, p.235). E-service quality is defined
broadly to encompass all phases of a customer’s interactions with a corporate which that is base on net work and
internet (Parasuraman et al., 2005, p.217).
Various dimensions and scales were developed to assess service quality in electronic fields. Parasuraman et
al. (2005) developed seven multiple-item scales (E-S-QUAL) for measuring the service quality. The basic E-S-
QUAL scale developed in four dimensions and the second scale, E-RecS-QUAL, is salient only to customers
who had nonroutine encounters with the e-systems (Parasuraman et al., 2005, p.213).
Table 1: Dimensions for measuring the e-service quality(Adapting from Parasuraman et al., 2005, p.220).
E-S-QUAL
1. Availability: The ease and speed of accessing and using the e-services.
2. Fulfillment: The extent to which the corporate’s promises are fulfilled.
3. Efficiency: The correct technical functioning of the e-services.
4. Privacy: The degree to which the site is safe and protects customer information.
E-RecS-QUAL
1. Responsiveness: Effective handling of problems.
2. Compensation: The degree to which the site and systems compensates customers for problem.
3. Contact: The availability of assistance through telephone or online representatives.
Technology Acceptance Model (TAM):
TAM, introduced by Davis (1986), is an adaptation of TRA specifically tailored for modeling user
acceptance of information systems. The goal of TAM is to provide an explanation of the determinants of
computer acceptance that is general, 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,p.985).
TAM posits that two particular beliefs, perceived usefulness and perceived ease of use, are of primary
relevance for computer acceptance behaviors (Figure 1). Perceived usefulness is defined as the prospective
user's subjective probability that using a specific application system will increase his or her job performance
(Davis, 1986, p.26). Perceived ease of use refers to the degree to which the prospective user expects the target
system to be free of effort (Ibid).
Theory of Planned Behavior (TPB):
TRA is used to predict an individual’s behavior only in a real voluntary situation, not in a mandatory
context (Li, 2008, p.6). Therefore, Ajzen (1991) develops the Theory of Planned Behavior to extend TRA to
consider the mandatory situation (figure 2). He adds a new construct of perceived behavioral control in TPB.
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J. Appl. Sci. Res., 8(3): 1842-1852, 2012
Perceived behavioral control is defined as “the perceived ease or difficulty of performing the behavior” (Ajzen,
1991, p.188). In the context of IS research, perceived behavioral control is defined as “perceptions of internal
and external constraints on behavior” (Taylor & Todd, 1995, p. 149).
Fig. 1: The Technology Acceptance Model (Davis et al., 1989, p.985).
Fig. 2: The Theory of Planned Behavior (Ajzen, 1991, p.181).
The Theory of Planned Behavior is similar to TRA in that TPB also assumes that individuals are rational
decision makers. Individuals assess perceived behavior control using a method similar to the expectancy-value
model. For each in a set of control beliefs, individuals multiply the belief’s strength by the perceived power of
the control factor (Li, 2008, p.6).
Research model and hypothesis:
According to Lee (2009), recently, a growing body of research has focused on integrating TAM and TPB to
examine e-service acceptance because the two models are complementary, and the results have showed that the
integration model had better exploratory power than the individual use of TAM and TPB. Taylor & Todd (1995)
and Shih & Fang (2004) show that integration models better explore the factors influencing adoption of e-
banking. Therefore, in this study, integration model of TAM and TPB was used.
Bagheri et al. (2009) have shown significantly direct influences of perceived ease of use, perceived
usefulness and trust on attitude. In the meantime, their research has shown attitude has a direct effect on
intention to use internet banking. The results of Shahrodi & Sayad Azari (2010) shown perceived behavior
control has positive influence on subjective norm and intention to use internet banking. Subjective norm has
positive influence on attitude towards use internet banking. However, subjective norm hasn’t significantly direct
influence on intention to use internet banking. According to Lee (2009), attitude towards use, perceived
usefulness, subjective norm and perceived behavior control have significantly direct influences on intention to
use internet banking. The findings of Shih & Fang (2004) indicated that an important factor influence the
intention to use is attitude towards use. However, subjective norm hasn’t significantly direct influence on
intention to use internet banking.
Studies show that when the customer is in direct contact with the technology there is greater control such as
with Internet banking. However, if there is an absence of direct contact, such as with telephone banking it is
assumed that there is less control perceived by the customer during this transaction (Joseph et al., 1999, p.183).
In the other hand, studies show that availability, efficiency fulfillment and Privacy -dimensions of e-service
quality) have positively influences the intention to use e- banking (Parasuraman, 2000; Liao & Cheung, 2002;
Akinci et al., 2004; Nezam Zade, 2009).
Based on theorical background, following research model and hypotheses are proposed.
According to the model, seven variables use to conduct this study: Electronic service quality (ESQ) with
four sub dimension, Perceived ease of use PEU), Perceived usefulness (PU), Attitude, Subjective norm (SN),
Perceived behavior control (PBC) and intention to use. Then, hypotheses of this research are following:
H1. E-service quality positively influences the intention to use e- banking.
H2. E-service quality positively influences the perceived behavior control.
H3. Perceived ease of use positively influences the perceived usefulness.
H4. Perceived usefulness positively influences the attitude towards the use of e-banking.
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H5. Perceived ease of use positively influences the attitude towards the use of e-banking.
H6. Perceived behavior control positively influences the subjective norm.
H7. Perceived behavior control positively influences the attitude towards the use of e-banking.
H8. Subjective norm positively influences the attitude towards the use of e-banking.
H9. Attitude positively influences the intention to use e- banking.
H10. Subjective norm positively influences the intention to use e- banking.
H11. Perceived behavior control positively influences the intention to use e- banking.
Fig. 3: The proposed research model.
Research method:
Research statistical population and sample:
Based on consulted opinions, Meli and Passargad Banks in Qom Province - Iran were chosen as the target
population for this study. Meli Bank is the largest and the most important governmental national bank in Iran.
This bank has been established in 1927. Passargad Bank is one of the most important privacy banks in Iran and
has been established in 2005.At present, Meli and Passargad Banks have, respectively, 49 and 2 branches in
Qom Province. Since, this study aimed to investigate effect of customers’ perceived e-service quality on
intention use e-banking; therefore, participants were required to have used e-banking services such as ATM,
Internet banking, Phone banking and Mobil banking prior. Thus, research statistical population consists of
customers of Passargad and Meli Banks in Qom Province that have used at least one of these e-banking systems
so far. The sampling methods of Meli Bank and Passargad Bank are, respectively, cluster and simple random
method. The customers were selected using simple random sampling. The number of sample respondents
(n=400) is considered suitable to apply SEM of analyses for the current study. Totally, 353 filled questionnaires
(180 and 173 questionnaires, respectively, from customers of Meli and Passargad Banks) were returned.
Data collection:
The study was conducted on 6 days over a 5-week period. The data was collected from 8 branches from the
Meli Bank and 2 from the Passargad Bank, counting up to 10 branches in total. From each branch, at least, 30
customers participated in this study. Totally, for collect e-banking customers’ information, distributed 400
questionnaires, with incomplete questionnaires deleted, result in 353 customers as sample size of this study (rate
of 88%). The demographic profile of the participants is provided in Table 2.
As shown in Table 2, the majority of respondents (56.7%) did not have using e-banking as major method of
performance banking transactions. 37.4% of the respondents were under 20 years old. 70% of the respondents
were male and 30% were female. Finally, the education levels of respondents were 65% college, about 10%
master & PhD and 25% high school.
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Table 2: Sample demographics.
Percentage (%) Frequency Item Measure
37.4 132 Under 25 Age
28 99 25-35
19.5 69 35-45
15 53 >=45
70 274 Male Gender
30 106 Female
24.9 88 High school Education level
65 230 College/university
9.9 35 Master & PhD
56.7 200 Attendance Major method of performance
his/her banking transactions
28.6 101 ATM
9.9 35 Internet banking
3.1 11 Phone banking
1.7 6 Mobile banking
Measurement:
A three-part questionnaire was designed to conduct this study: (1) basic information about respondents’
characteristics including gender, age, education and major method of performance banking transactions; (2)
customers’ perceived e-service quality (4 dimensions and 14 items adapted from basic scales defined by
Parasuraman et al. (2005); and (3) questions to assess the constructs of perceived usefulness, perceived ease of
use, attitude, subjective norm, perceived behavior control and intention to use were adapted from the
measurements defined by Cheng et al. (2006) , Wu & Chen (2005), zhu et al. (2002) and Lee(2009) containing
21 items. Finally, with considered Panel of Judges modified the questionnaire to e-banking field.
The first part includes nominal scales, and the remainder were measured using the five-point Likert scale,
ranging from strongly disagree (1) to strongly agree (5).
Before conducting the main survey, pilot test was performed to reliability the instrument. The pilot test
involved 35 respondents. Cronbach’s alpha scores shown in Table 3 indicated that each construct exhibited
strong internal reliability. The expert opinions were sought for in order to content validity of the questionnaire
involved 20 experts. Factor analysis was used to factor validity of the questionnaire (see Table 3). Therefore, the
instrument has confirmed reliability and validity.
Result:
This paper followed the two-step procedure suggested by Anderson & Gerbing (1988): (1) the measurement
model was examined; (2) the structural model was analyzed. Therefore, in order to analyzing the collected data,
SPSS16 and AMOS20 soft wares were used.
Since e-service quality has four sub dimensions: 1.Availability (3 items); 2.Privacy (3 items); 3.Efficiency
(4 items) 4.Fulfillment (4 items), second-order CFA was carried out (Error! Reference source not found.).
The model shows that all of the factor loadings are more than .5 and fit indices are acceptable except P-
value and GFI (see Figure 4).
Having had the second-order CFA homogenous parcelled to four items, the measurement model changes to
Error! Reference source not found..The purpose was to reduce the number of variables and simplify the
subsequent structural equation analysis of the proposed model.
Analysis of the measurement model:
Before evaluating the fit of the structure model, it was necessary to define a measurement model to verify
that the 35 measurement variables written to reflect the seven unobserved constructs (Perceived usefulness,
Perceived ease of use, Subjective norm and Perceived control behavior, Attitude, Intention and e-service
quality) do so in a reliable manner. The overall fit of a measurement model was determined by a CFA.
The results of reliability (Table 3) showed all 7 constructs of Cronbach’s alpha have satisfactory values,
ranging from 0.816 to 0.832, indicating acceptable levels of internal consistency. Similarly, the coefficients of
composite reliability have satisfactory values. The lowest score of composite reliability in the current study was
0.702. According to Fornell and Larcker (1981) and Hair et al. (2006), these scores indicated evidence of
reliability. All indicator factors loading should be significant and exceed 0.5 (Fornell & Larcker, 1981 & Hair et
al., 2006). The factor loading values of the measurement model exceeded 0.5. Therefore, convergent validity
was met.
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Fig. 4: Second-order CFA for e-service quality dimensions.
Fig. 5: Homogenous parceling method for service quality factors.
Discriminant validity assesses the extent to which a concept and its indicators differ from another concept
and its indicators (Bagozzi et al., 1991). According to Byren (2010), the correlations between items in any two
constructs should be lower than 0.9. Correlations between the construct and any other construct in the proposed
model in this study were lower than 0.9, satisfying Byren (2010) criteria for discriminant validity. Thus,
construct validity was met (see Table 4).
Analysis of the structural model:
Final model shown in figure 6 (Which dotted line represents no significance)
For assess the goodness-of-fit the chi-square test and other fit indices (e.g., GFI, AGFI, CFI, NFI, and RFI)
were considered. The CFA results presented an acceptable level of fit .Chi-square to the degree of freedom of
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SEM (χ2 /df) of 1.139 (p < 0.05), goodness-of-fit index (GFI) of 0.937, Tucker-Lewis index (TLI) of
0.967adjusted goodness-of-fit index (AGFI) of 0.923, comparative fit index (CFI) of 0.971, normed fit index
(NFI) of 0.807, and root mean square error of approximation (RMSEA) of 0.02 (see Figure 6). Generally,
according to Bagozzi et al. (1991); Hair et al. (2006); Ho (2006) and Byren(2010), the CFA results presented an
acceptable level of fit. Furthermore, RMSEA values up to 0.08 are acceptable (Hair et al., 2006); therefore, the
RMSEA value was acceptable.
Table 3: Construct reliability and convergent validity.
Cronbach’s alpha Composite
reliability
Factor loading Item Construct/indicator
0.817 0.873 0.800 Availability E-service quality
0.816 0.761 Privacy
0.817 0.811 Efficiency
0.817 0.763 Fulfillment
0.828 0.736 0.514 PEU1 Perceived ease of use
0.6 PEU2
0.525 PEU3
0.832 0.712 0.557 PU1 Perceived usefulness
0.594 PU2
0.537 PU3
0.829 0.703 0.539 AT1 Attitude
0.561 AT2
0.526 AT3
0.516 AT4
0.827 0.736 0.590 SN1 Subjective norm
.0552 SN2
0.511 SN3
0.824 0.742 0.506 PBC1 Perceived behavior control
0.535 PBC2
0.551 PBC3
0.554 PBC4
0.513 PBC5
0.828 0.702 0.513 IT1 Intention
0.676 IT2
0.550 IT3
Table 4: Correlations matrix of variables(Discriminant validity).
7 6 5 4 3 2 1 Construct
1 1. E-service quality
1 0.080 2.Perceived ease of use
1 0.223 0.00 3. Perceived usefulness
1 0.059 0.211 -0.031 4.Subjective norm
1 0.365 0.151 0.038 0.081 5.Perceived behavior control
1 0.247 0.067 0.159 0.170 0.178 6.Attitude
1 0.268 0.203 0.123 0.110 0.345 0.269 7.Intention
Path analysis and hypotheses testing:
Fig. 6 presents the results of the structural. Intention to use electronic banking in this study was jointly
predicted by ESQ (β = .223, Standardized path coefficient, p < 0.01), and subjective norm (β = 0.277, p < 0.05)
and these variables together explained 60% of the variance of intention to use (R2 = 0.60, coefficient of
determination). As a result, Hypotheses 1and 10 were supported. Attitude was predicted by PEU (β = 0.203, p <
0.05) and SN (β = .372, p < 0.01). Together these variables explained 70% of the total variance of Attitude.
These findings validated Hypotheses 4 and 8. PEU (β .218, p < 0.05) significantly influenced perceived
usefulness while explaining 47% of the total variance in perceived usefulness. Accordingly, Hypothesis 3 was
supported. PBC (β = .253, p < 0.01) significantly influenced subjective norm while explaining 64% of the total
variance in subjective norm. Consequently, Hypothesis 6 was supported. ESQ (β =.181, p<0.05) significantly
influenced perceived behavior control while explaining 33% of the total variance in perceived behavior control.
Accordingly, Hypothesis 2 was supported. Hypotheses results are shown in Figure 6.
The level of ESQ had significant indirect effect (β=.042, p < 0.05) on intention to use e-banking, suggesting
the important mediating effects of PBC. The level of PBC had significant indirect effect (β=.071, p < 0.05) on
intention to use e-banking, suggesting the important mediating effects of subjective norm. To further assess the
significance of indirect effects of predictor variables on intentions to use e-banking, a decomposition of the
effects analysis was conducted (see Table 5).
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Fig. 6: Final model.
* P<0.05
** P<0.01
Note: dotted line represents no significance
Table 5: Direct, indirect and total effects– estimates.
Intention to use Variable/ predictors
Total effects Indirect effects Direct effects
.265
**
.042
*
.223
**
E-service quality
-.021 -.021 Perceived usefulness
.003 .003 Perceived ease of use
.018 .018 Attitude
.283
**
.007 .277
*
Subjective norm
.220
*
.071
*
.159 Perceived behavior control
*
Significant at p < 0.05
**
Significant at p < 0.01
Discussion:
The results of SEM confirm fitness of the research model presented in Figure 1. Therefore, the integrated
model of TAM with TPB model with e- services quality is capable of explaining factors influence the intention
to use e-banking. Several results could be drawn from this research that presented below:
Subjective norm and electronic services quality emerged as direct factors in the intention to use electronic
banking and these variables together explained 60% of the variance of intention to use and 40% of the variance
of intention to use is explained by other factors which in this study haven’t been considered. The effect of
subjective norm on intention to use electronic banking (β=.277) is more than that of electronic services quality
(β=.223). Therefore, subjective norm appears to be the more important direct predictors to intention to use
electronic banking. This result is similar to the finding reported in Lee (2009), which indicated that subjective
norm has direct influences on behavioral intentions toward e-banking use. Likewise, the authors such as
Parasuraman (2000), Liao & Cheung (2002), Akinci et al. (2004) and Nezam Zade (2009) show that dimensions
of e-service quality have positively influences the intention to use e- banking. Despite of this result, Shih &
Fang (2004) indicated that subjective norm has non-significant influences on behavioral intentions toward e-
banking use.
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Second, perceived behavior control and e-services quality emerged as indirect factors in the intention to use
electronic banking. The indirect effect of perceived behavior control on intention to use electronic banking
(β=.071) is more than that of e-services quality (β=.042). This implies that the perceived behavior control
appears to be the more important indirect predictors to intention to use e-banking. Both studies reached the same
conclusion that perceived.
Third, e-services quality has direct and indirect effect on intention to use electronic banking and its direct
(β=.223) is more than its indirect effect (β=.042).
The forth finding reveals that other constructs of hypothesized model (PU, PEU and Attitude) didn
’
t have
significantly direct or indirect influence on intention to use. Despite of this result, Lee (2009) indicated that PU,
PEU and attitude have significant influences on behavioral intentions toward e-banking use. Eze et al.(2011),
despite of results of this study, shown that PU and PEU have significant influences on intentions toward e-
banking use and Shih & Fang(2004) indicated that attitude has significant influence on intentions toward e-
banking use.
Academician and managerial implications:
The results of present study reveal some issues related to customer intentions to e-banking that have not
been addressed by studies yet. Specially, these finding are notable for banks managers as they decide how to
allocate resources to encourage their customers to use the e-banking services by promote e-services quality.
Since building a qualified e-transaction environment is much more important to customers, banks need to
promote quality of e- banking systems.
This study suggests that they should consider focusing on the power and speed of e-banking systems. As
result shown, mobile bank isn’t welcomed by customers, it probability, because of, mobile banking is the new e-
banking system. Thus, banks could increase familiarity and explain its advantages through advertising.
Likewise, this study suggests that they should standardize soft ware and hard ware of e-banking systems. In
order to increase customers’ knowledge and ability to use e-banking, banks should held training courses about
how to use e-banking systems.
On the other hand, present study attempts to develop a new model by integration of two models with
service quality. It is important to note that the new variable – perceived electronic services quality – is
compatible with the TAM and TPB variables, and the overall proposed model confirms. Hence, the results of
this study contribute a significant implication for developing theories to e-banking area and the proposed model
makes an important contribution to literature on e- banking. The present study has many implications for future
e-banking research.
First, in the present study casual relationship between e-service quality and intention to use was
investigated, however, it is notable to study the casual relationship between dimensions of service quality on
intention of use.
Second, although TAM has been found an appropriate and robust model by some prior research, it only
includes three variables to predict intention to use. However, behavioral intention will also be affected by
various factors. Thus, according to Lee (2009) integrated model of TAM and TPB are complementary
approaches. Moreover, this study followed Lee (2009) suggested a basis for the integration of other technology
acceptance models.
Third, as mentioned earlier, although factors influence the adopting online banking -as one of the e-banking
systems- have been studied by many researchers, limited studies conducted regarding all of the e-banking
systems. This study conducted regarding all of the e-banking systems. Therefore, this is basis for employing
integrated models in the whole e-banking systems.
Finally, although consumer perceptions of various constructs of adopting online banking have been studied
by many researchers (Tan & Teo, 2000; Yousafzai et al., 2003; Lee, 2009), yet, e-service quality has not been
studied by any researchers. At first time, this paper studied e-service quality as a factor that influences the
adopting e-banking.
Conclusions, limitation and future research:
In addition to the implications for management, this research also makes strides in addressing theoretical
and practical issues in e-banking research.
The purpose of this paper is to develop an extended TAM with TPB model to predict and explain
customers’ behavioral intentions to e- banking. The proposed model incorporates e-service quality. The results
of structural equations modeling confirm model good fitness in predicting customer’s intentions to use such
services. The data from this study indicated positive effect of e-service quality on intention to use e-banking
systems.
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This study, as with any research, has some limitation. Thus, it should be considered that generalization of
the findings is limited to similar conditions. First, the geographical area in this study was constraint. The online
survey method was appropriate for collecting data from participants with Internet experience and who were free
of geographical constraints. Second, the results drawn from current study are based on cross-sectional data.
Future researchers could employee a longitudinal study to evaluate this aspect.
Third, the respondents in this study were limited to customers of Iranian banks. The branches of banks were
from branches of two Banks in Qom Province. However, similar study could be conducted with considered all
banks (and branches).
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