INTERNET BANKING USING INTENTION: A MODEL MODIFICATION BASED ON QUALITY AND RISK CHARACTERISTICS
Mujilan1 and
Sumiyana2
Abstract
This study modifies the Internet Banking (IB) Using Intention Model based on quality and risk. This study also gives the alternative model by dimension modification especially in the quality. The result of this study indicates the two types of quality impact to the intention. If we apply 11 dimensions of quality, there is a strong direct effect of the IB service quality to the intention. But if we apply the general perception of quality, the effect of IB service quality to the intention will be indirect via the mediating of user satisfactions. Risk has a low negative impact to the intention if the users recognize the IB service quality and satisfy which the services. It means that quality and satisfaction plays an important role to reduce perceived risk and build intention in the internet banking context. This modified model can be used to explain the characteristics of users in their perception of internet banking.
Keywords:internet banking, service quality, perceived risk, user
satisfaction, intention to use, internet users, quality dimensions, general perceived quality.
INTRODUCTION
The internet banking using has grown as a consequence of internet and technology
growth in the world. The success model of internet banking (IB) had the own
characteristics that difference in some way from others technology success models like
web portals or e-commerce (Bauer et al., 2005). Consequently, there is a need to develop
an internet banking success model to explain the IB characteristics.
Researchers still focused on internet banking quality (eq. Bauer et al., 2005; Ma et
al., 2011) and some studies focused on internet banking risk (eq. Wong et al., 2009;
Aslam et al., 2011). In their studied, Ma et al. (2011) suggested that the internet banking
service quality should be extended to how user’s perception of internet banking service
quality affected the satisfaction and intention behavioral. This study modifies the internet
banking model to extend Ma’s internet banking service quality and to integrate which
1
Mujilan is an education staff in Accounting Department, Faculty of Economics, Widya Mandala Madiun University; email: agus_muji@yahoo.com
2
Sumiyana is accounting professor in Faculty of Economics and Business, Gadjah Mada
DeLone & McLean technology success model validated by Wang (2008). We also
investigate perceived risk on using internet banking (Aslam et al., 2011) to complete the
internet banking model.
To modify the model, we approach from the human’s rational judgment in the
decision making process. Two basic factors are considered as cost and benefit.
Consequently, the model will include this two factors called internet banking quality and
risk. Operationally, we use Internet Banking Service Quality (Ma et al. 2011) and
Perceived Risk (Aslam et al., 2011). The SEM by Amos 18 is applied to test the model.
This study focuses on integrating four factors to modify the model. The factors are
quality, satisfaction, risk, and intention. The path directions are investigated and
explained. We collect instrument to measure this four factors from some literatures and
simplify the item numbers to reduce the response bias. We hope this study will contribute
to the information system literature by support the intention of using internet banking
model and understanding of how the effect direction of each factors. Also, we hope to give
the simplified instrument related to the model.
This study is a general view of the internet banking model. A specific characteristic
(eq. respondents, IB providers) may need in the future study. The context of this study is
individual perception (not a company) and be done in the developing country. This study
has assumptions, first, the respondents use rational consideration in decision making to
use internet banking. Second, individuals use their IB to operate their saving account.
Third, this IB is used in developing country or in the early phase of internet using
(Gounaris and Dimitriadis, 2003). Forth, the distance from the user’s home to the
conventional banking office doesn’t affect the decision in using IB.
LITERATURE REVIEW
Internet Banking Service Quality
Service quality is become the great differentiator, the most powerful competitive
weapon most service organization possess (Berry et al., 1988; Jayawardhena, 2004).
Quality from customer’s view is conformance to specification (Berry et al., 1988). The
customer’s retention and interest is very depended on services quality delivered (Hamadi,
2010).
Quality has been viewed by Garvin (1987) as a product quality. In the information
systems there was service quality (SERVQUAL) which five dimensions (Parasuraman et
al., 1991; Kettiger & Lee, 1997). The context of service quality was used by Gounaris &
of e-banking. Jayawardhena (2004) studied in internet banking quality measurement.
Yaya et al. (2011) used E-S-QUAL (modification from SERVQUAL by Parasuraman et al.,
2005) to evaluate online banking.
Ma et al. (2011) tried to identify the internet banking quality dimensions from former
literatures. They used 11 quality dimensions. They applied the dimensions as variables in
the regression. The dimensions were: reliability, convenience, efficiency, comfort,
serviceability, security, privacy, assurance, reputation, product differentiation and
customization, and customer service.
Reliability: if it delivers the services as it’s promised (Ma et al., 2011). Reliability is
the ability to perform the desired service dependably, accurately, and consistently (Berry
et al., 1988; Parasuraman et al., 1985; McKinney et al., 2002; Kenova & Jonasson, 2006).
Convenience: if it enables customers to access banking at all times and places (Ma et
al., 2011). Wolfinbarger & Gilly (2001) used convenience in the context of saving time and
effort, including physical and mental effort.
Efficiency: Ma et al. (2011) used efficiency in the context of speed download and
response time. Efficiency also be used as the site is simple to use, structured properly,
requires minimum of information to be input by the customer (Kenova & Jonasson, 2006;
Parasuraman et al., 2005). Then, efficiency is described as ease and speed of accessing
and using the site (Parasuraman et al., 2005). Comfort: Hong et al. (2011) referred to
psychology that comfort was a feeling at ease. Operationally, the comfort if the user feel
comfort with the change in upgrade system. Ma et al. (2011) used this context to cap that
the user comfort with internet banking.
Serviceability: is innovation ability to confirm the users need (Ma et al., 2011).
Garvin (1987) defined serviceability as speed, courtesy, competence, and easy of repair.
Security: is defined as financial security, refer to the fact that user has perception if their
bank information is secure and no one else can access their account (Hamadi, 2010).
Privacy:is the user level perceptions that their personal information is protected (Hamadi,
2010). In other word privacy is the state level if the site safe and protecting the user
information (Parasuraman et al., 2005). Privacy is related to the secure of private and
secret information (Ma et al., 2011).
Assurance: can be understood as the employee knowledge, courteous, and the
ability to make users feel confidence (Kettinger & Lee, 1997). It can be related to the
clearance and trustfully of the information (Kenova & Jonasson, 2006). Reputation: Ma et
al. (2011) took a marketing context that reputation was associated which brand equity, or
relations in their function. Credibility was obtained from trustworthiness, believability, and
honesty (Parasuraman et al., 1985).
Product differentiation and customization: Ma et al. (2011) used it as the
adoption of website to the better setting according to individual user requirements. Swaid
& Wigant (2009) used it as a user perception of the individualized attention and
differentiated services that were tailored to meet individual’s need and preferences. Cruz
& Gallego (2004) said that the personalization systems could be categorized as
customization (user personalization) and based on user profile (adaptive and proactive
configuration). Customer services: The online customer service may hide from users.
This will be a relation to the emotion and feeling of the users because no human
interaction. Parasuraman et al. (2005) used contact dimensions to explain the assistance
via telephone or online representatives.
User Satisfaction
Satisfied customers may pass on positive comments about the firm and its offering
and recommend the company to others (Zeng et al., 2009). In the online shopping
context, McKinney et al. (2002) define satisfaction as an affective state representing an
emotional- reaction to the entire web site search experience. Based on McKinney et al.
(2002) we define user satisfaction of internet banking as an affective state representing an
emotional reaction to the internet banking site experience.
Perceived Risk
Wong et al. (2009) in the online shopping context defined perceived risk as
customer perceptions to the risk of internet transaction. This study uses operational
definition of perceived risk in internet banking as user perceptions of the risk when
adopting internet banking.
There are some barriers in internet banking adoption perceived by the active
internet users, especially in the developing countries (Aslam et al., 2011). This barrier can
reduce the intention to adopt the internet banking. In their studied, Aslam et al. (2011)
categorized two major barriers: psychological barriers, and technical barriers. In the
psychological barriers there were low perceived value and high perceived risk. This
perceived risk included economic risk, functional risk, social risk, and psychological risk.
The second major barrier was technical barrier included lack of security and privacy, lack
of knowledge, and access to internet.
In this study, we adopt perceived risk from Aslam’s high perceived risk. So there will
be economic risk, functional risk, social risk, and psychological risk. Economic risk
operating difficulty and chances of incomplete transactions due to internet speed failure.
Social risk is related to the culture that in the developing countries have soft and
collectivist culture where concern for social value. Unlike traditional banking, using online
banking is perceived to hinder the social relationship during the transaction based on
interpersonal interaction at the physical services. Psychological risk is about psychological
inconvenience while switching from conventional banking habits to the online banking
system and to learn new technology impedes the adoption process.
Intention to Use
Wang (2008) applied Intention to Reuse when modify DeLone & MacLean Model
(2003). DeLone & McLean (2003) suggested that Intention to Use may be a worthwhile
alternative measure in intention to reuse context. Based on the marketing literature, Wang
(2008) defined Intention to Reuse as the favorable attitude of the customer toward an
e-commerce system that results in repeat used/purchased behavior. Based on Wang
(2008), we raise an operational definition of internet banking Intention to Use as favorable
attitude of the internet user towards an internet banking system that result in use behavior.
This concept can be applied when we investigate the IB users or potential users.
Variable Relationships
Wang (2008) indicated that quality in the information system had positive direct
effect to the user satisfaction. Zeng et al. (2009) gave the same indication; they said that
fulfillment/reliability had direct effect to the overall satisfaction. This fulfillment variable is a
part of the quality dimension, and overall satisfaction is close to the user satisfaction. In
the internet banking context the quality will have a positive impact to the user satisfaction.
This is alike direction in the other web context. It’s predicted if internet banking can
provide the quality which match to the user’s expectation, they will feel that their need is
complied, further they will be satisfied with the internet banking services.
In the e-commerce and online shopping context, user satisfaction had an effect to
the intention (Wang, 2008; Zeng et al., 2009). We think internet banking characteristic is
closely to e-commerce context. Satisfaction is a symbol that user expectation complied
and they feel comfort in using internet banking. This satisfaction affect emotionally than
users will not reluctant to use internet banking. Implicitly this says that quality has impact
to the intention via user satisfaction.
If Wang’s and Zeng’s research show the indirect effect from quality to the intention,
Hamadi (2010) gave the evidence in the internet banking context that perceived quality
had stronger direct effect to the commitment than via mediation of satisfaction. We think
intention to revisit the bank site. The explanation of this direction is customers do business
via bank website if they think internet banking has good quality although they do not be
satisfied at all components.
Wong et al. (2009) found that perceived risk had negative direct effect to the
willingness to use e-banking. In other way Zeng et al. (2009) said that variable
security/privacy which indicators of risk perceptions on using online transactions had
effect to the repurchase intention. The willingness to use e-banking or repurchase
intention are closely to the intention to use in this study. It seems clear that perceived risk
has negative impact to the intention. Ones who perceive higher risk in the internet banking
will lower the intention to use. May they choose to visit the physic bank or use the other
usual facilities used.
Other evidence related to the risk is that risk can be reduced by raise some quality
in dimensions (Chen & Chang, 2005; Lee et al., 2010). Chen & Chang (2005) gave the
explanation, “Theoretically, if service quality cues in an advertisement indicate the service
will be performed at a high level, the associated risk should be reduced. If customers feel
a service firm is reliable (for example, possessing adequate and up-to-date equipment),
responsive to their particular requests, reassuring, and empathic in caring for them as an
individual, then the risk of patronizing that service should be reduced.” This explanation
indicated that quality has negative direct effect to the perceived risk.
Because quality has direct impact to the satisfaction and perceived risk, so that will
be an implicit hypothesis that satisfaction give a negative direct effect to the perceived
risk. This direction is an implication from the indirect effect of quality to the perceived risk
via user satisfaction.
H1: Internet Banking Service Quality has a positive impact to the user satisfaction.
H2: User satisfaction has a positive impact to the Intention to Use
H3: Internet Banking Service Quality has a positive impact to the Intention to Use.
H4: Perceived Risk has a negative impact to the Intention to Use
H5: Internet Banking Service Quality has a negative impact to the Perceived Risk.
H6. Internet Banking Service Quality has a negative impact to the Perceived Risk.
RESEARCH METHOD
Sampling and data collection
The respondents were internet users in Indonesia conducted by paper and online
media. First, post mails were sent to 15 Catholic Universities (island of Java, Sumatra,
department to the lectures in scope. The questioners are also sent to accounting
department of 10 companies in Java. Second, the online survey was conducted to fulfill
the questioner on the web. Post’s mail invitations were sent to 16 companies in Java.
Online invitations were sent privately via e-mail, facebook, and tokobagus. Overall 1831
paper and online questioners were sent. Responses were 429 (23.4%) questioners back
or fulfilled. The data was checked and found 2 double IP, 6 un-complete, 14 un-seriously.
Used data is 407 (22%).
Instruments
Questioner in 7 point Likert-scale was applied. The variable indicators were
collected from some literatures which 73 items available. We used 44 items (see
appendix) to reduce response bias because of so many questions. The items reducing
were based on judgment: properly to the internet banking, same dimensions, clearly
statement specification. Confirmatory factor analysis was applied by SPSS-dimension
reduction and scale reliability. Loading factor were in range of 0.60 - 0.95. Sixth indicator
of perceived risk (ris06) was dropped because consist in cross loading. Cronbach’s alpha
for variables and quality dimensions were in range 0.72 – 0.93. So the instruments were
valid and reliable according to the data characteristic in this study.
Data and Model Testing
The models were tested by SEM which Amos 18 software. Measurement model for
confirmatory factor analysis, normality, and outliers were accessed to understanding the
data and model characteristic. Model fit is accessed by Goodness-of-Fit Index (GOF
Index) included absolute measures, incremental fit measures, and parsimony measures
(Hair et al., 2010: p. 716).
RESULT, ANALYSIS, and DISCUSSION
Demography
The demography data is shown in the table 1. Using frequencies indicate the
prediction of IB using every month. The dominant users are in the level 1 (1 till 5 times).
The purpose of IB using is dominant for private need. The respondent is dominant from
employee in the universities or companies. The respondents were male 57% and female
Table 1: demography
0 1 (1‐5) 2 (6 ‐ 10) 3 (> 10)
Using Frequencies 96 211 28 72 407
Using Purpose
1‐Private 190 15 30 235 58%
2‐office/business 21 13 42 76 19%
Job status
1‐Employee 74 175 23 62 334 82%
2‐Entepreneur 3 17 5 10 35 9%
3‐High student 19 19 38 9%
Gender
1‐Male 59 102 21 49 231 57%
2‐Female 37 109 7 23 176 43%
N Categories Using Frequencies (in a month)
Data Examining
Normality test for the data model shows t value out of range + 2.58. It significance
<0.5 or indicated non-normal distributed. There is explanation about the normality on
Likert-scale. Clason & Dormody said it was difficult to see how normally distributed data
can arise in a single Likert-type item. The data will frequently be skewed. Further,
Norman (2010) said that parametric statistics could be used with Likert data, with small
sample size, with unequal variance, and with non-normal distributions, with no fear of
coming to the wrong conclusion. Another consideration on Likert-type was a large sample
size could be assumed had a normal distribution. For sample sizes of 200 or more, the
effect of non-normality may be negligible; the researcher could be less concerned about
nonnormal variables (Hair et al., 2010: 71).
Measurement models are applied in the two categorizes: first, measuring the quality
which it’s dimensions. Second, is measuring the satisfaction, perceived risk, and intention.
Quality measurement model indicated that loading factor is greater than 0.5.
Measurement model for satisfaction, risk, and intention resulted loading factor was greater
than 0.6. This value indicates a good convergent validity. The discriminant validity is
accessed from correlation matrices. All indicators show higher correlation values to the
own variable compared to other variables. It indicates good discriminant validity.
In these measurement models there are a correlation or affection among error term
or indicators suggested by Amos modification model. Not all of these suggestion were
used because insignificant or to avoid a more complex path models. Example, the
intention to visit website (int01) reduced risk of computer cost (ris01) but arise the
was intention to increase the using IB in the future (int03) would reduce the risk of
difficulty to learn the interface (ris07).
Structural Equation Model (A)
Fit indices of Model A can be seen at table 2. The Model A has χ2 value 3353.130
sig 0.000. It significance of χ2 indicates not a good fit model, but we can see the ratio χ2/df
(3353.130/721 = 4.651), ratio < 5 indicated that model is fit. RMSEA also support this fit
which mediocre category, value 0.095. Furthermore, GFI (0.726), AGFI (0.672) and PGFI
(0.608), it values are greater than 0.6 or indicate a good fit. CFI (0.816) is near to 0.9.
Generally can be concluded that model is good but hasn’t perfected.
Comparison Model (B)
In the instrument we have two indicators that measure the internet banking service
quality in general. The indicators were used by Ma et al. (2011) to test the antecedent of
quality variable. Model B or Model Alternative uses the way which this two quality
indicators to replace 11 dimensions of quality. The result of Model B can be seen at table
2 column B. Although the Model B show better fit which less χ2 ratio, less RMSEA, greater
GFI, and greater CFI, but we recommend the Model A to used in the future because it has
Figure 1: Testing Models for intention to use internet banking
Model A: which 11 IB quality dimensions Model B: which general IB perceived quality
Table 2: Comparison of GOF Models
GOF Index Standard Mod. A Mod. B (alt)
N 407 407
Absolut Fit Indices
Chi‐Square (X2) 3353.130 312.420
Degree of freedom 721.000 82.000
Probability P > 0.05 .000 .000
Rasio X2/df < 2 ; 5 4.651 3.810
RMSEA < 0.05; 0.08; 0.1 .095 .083
GFI >0.6 .726 .915
AGFI >0.6 .672 .860
PGFI >0.6 .608 .552
RMR <0.08 .089 .065
Incremental / Relative Fit Indices
NFI > 0.9 .778 .940
NNFI / TLI ‐‐>1 .791 .934
RFI >0.9 .748 .912
IFI >0.9 .817 .955
CFI ** >0.9 .816 .955
Parsimony Fit Indices
PRATIO .879 .683
PNFI 0 – 1 .684 .642
Table 3: Comparison of Standardized regression weight
Endogen Exogen Coeff. CR Sig. Coeff. CR Sig.
Satisfaction <‐‐‐ Quality H1 + .840 13.508 *** .858 18.757 *** Intention <‐‐‐ satisfaction H2 + .242 3.242 *** .419 4.518 ***
Intention <‐‐‐ Quality H3 + .551 6.793 *** .333 3.608 ***
Intention <‐‐‐ Risk H4 ‐ ‐.081 ‐2.005 ** ‐.077 ‐1.793 *
Risk <‐‐‐ Quality H5 ‐ .101 .958 .120 .995
Risk <‐‐‐ satisfaction H6 ‐ ‐.452 ‐4.253 *** ‐.468 ‐3.889 ***
*** sig. 1%; ** sig. 5%; * sig. 10%
Hyphotheses/ Predictions
Variables Mod A Model B (alt)
The score of standardized regression weight in table 3 were drawn in the path
diagram as shown in figure 2. This comparison data from the table or the figure will be
used to conclude the hypotheses. The main consideration is result from the Model A.
Table 3 indicates only one hypothesis unsupported, it is H5 which predict a negative
impact from the quality to the risk. We can see that the result is positive insignificance
(coeff. 0.101; CR = 0.958).
Hypothesis 1 predicts that IB service quality has a positive impact to the user
satisfaction. Model A show coefficient 0.840 sig 1%.. So it can be concluded that
hypothesis 1 is supported. Hypothesis 2 predicts that user satisfaction has a positive
impact to the intention to use. The coefficient of this link in Model A is 0.242 at
Figure 2: standardized regression weight (compared model A, B)
Hypothesis 3 predicts that quality has a direct positive impact to the intention. The
result shows that all models have a positive direct impact from quality to the intention
(0.551; 0.333) sig. 1%. So, hypothesis 3 is supported. Hypothesis 4 predicts that
perceived risk has a negative impact to the intention to use. The result supports this
hypothesis. The impact values are -0.081, -0.077. Hypothesis 6 predicts that user
satisfaction had a negative or reducing perceived risk. The result of all model indicate a
negative impact (-0.452; -0.468), the hypothesis 6 is supported.
Discussion
The link between quality and risk shows in-significance result and contra prediction.
The hypothesis predicts that quality will reduce risk, but the result shows the quality hasn’t
power to reduce the perceived risk. In this case we can see the role of user satisfaction in
reducing risk. When the quality has no power to reduce risk, the user satisfaction does.
User satisfaction play an important role by mediates the link between quality and risk.
Users reduce their risk perception only if the quality makes them be satisfied. Other
researches (eq. Chen & Chang, 2005; Lee et al., 2010) saw the direct effect of quality to
Even though risk has a negative impact or reduce the intention, but this impact is
lower than impact of quality or satisfaction. This indicates that in the user’s perception of
quality and satisfaction is more important than the risk.
The impact of quality to the intention has a differ value by Model A and B. Model A
shows higher value than B in the direct effect of quality to intention. But, when there is a
mediating of user satisfaction, Model B shows higher value of the path user satisfaction to
the intention. This can be explained that when users know the specification of quality (by
11 quality dimensions) that will be direct impact to the intention. But if users only be asked
by the general perception of quality (2 quality indicators), they need satisfy first so the
intention will arise. The mediating effect of satisfaction has an important role when users
only know the general perception of quality.
CONCLUSSIONS AND LIMITATIONS
The model of internet banking using intention is modified by the rational
consideration in decision making approach which in this study uses the quality and risk.
The model has four variables: internet banking service quality, user satisfaction, perceived
risk, and intention to use. The model which eleven dimensions of quality was applied in
model A. Then, model B used the general perception of quality by two items of indicator.
The two models have a good fit, but have a little different in the effect of quality to the
intention. Users who know the specific quality feel the direct impact to the intention, but if
users only know the general perception of quality that the satisfaction will have an
important role. Perceived risk has a negative impact to the intention, but this impact is
lower power than the quality and satisfaction. Perceived risk can be reduced by raising the
quality, but if users feel be satisfied first by the quality offered. So, there is a mediating
effect of user satisfaction.
The decisions by the result of this study must be considered by the limitations. First,
this study is individual perceptions by self reporting from a survey and dominant from an
employee. Second, the internet users are in the category of earlier level of adoption.
Future researches can be done in the context of another user area, specific IB provider,
user using frequencies characteristics, and IB using for companies adoption. Researcher
can investigate deeper to the dimensions of quality. We suggest that the instrument in this
study can be considered to be applied because more simple by the item size. We suggest
References
Aslam, H.D., Khan, M. and Tanveer, A. (2011), Perceive Barriers Towards Adoption Of Internet Banking Among Non-Metropolitan Internet Users of Pakistan, International
Business & Economics Research Journal. Vol. 10, No. 4, pp. 45-55
Bauer, H.H., Hammerschmidt, M. and Falk, T. (2005). Measuring the quality of e-banking portals. International Journal of Bank. Vol. 23 No 2, pp. 153-175.
Berry, L.L., Parasuraman , A. and Zeithaml, V.A. (1988), The Service-Quality Puzzle,
Business Horizons, Sept – Oct 1988, pp. 35-43.
Chen, T. and Chang, H. (2005), Reducing Consumer’s Perceived Risk Through Banking Service Quality Cues in Taiwan, Journal of Business and Psychology, Vol. 19, No. 4, pp. 521-540.
Clason, D.L. and Dormody, T.J. (1994), Analyzing Data Measured by Individual Likert-Type Items. Journal of Agriculture Education, Vol. 35, No. 4, pp. 31-35.
Cruz, P.P.P. and Gallego, P.DM. (2004), Flow in eBank Satisfaction Measure,
Documentos de Trabajo “Nuevas Tendencias En Direccion de Empresas” DT 06/04,
pp. 1-10.
Garvin, D.A. (1987), Competing on the eight dimension of quality, Harvard Business
Review, Nov – Dec, pp. 101-109.
Gounaris, S. and Dimitriadis, S. (2003), “Assessing service quality on the web: evidence from business-to-consumer portals”, Journal of Services Marketing, Vol. 17 No. 5, pp. 529-48.
Hair, J.F.JR., Black, W.C., Babin, B.J. and Anderson, R.E. (2010), Multivariate Data
Anaysis, 7th edn, Pearson Prentice Hall.
Hamadi, C. (2010), The Impact of Quality of Online Banking on Customer Commitment,
Communications of the IBIMA, Vol. 2010, pp. 1-8.
Jayawardhena, C. (2004), Measurement of Service Quality in Internet Banking: The Development of an Instrument, Journal of Marketing Management, 20, pp. 185-207.
Kenova, V. and Jonasson, P. (2006), Quality Online Banking Services, Bachelor Thesis within Business Administration, Jonkoping International Business School.
Kettinger, W.J. and Lee, C.C. (1997), Pragmatic Perspectives on the Measurement of Information Systems Service Quality, MIS Quarterly, June, pp. 223-240.
Lee, A.T., Liou, S.N., Lee, A.P. and Cheng, YC. (2010), The Effects of On-line Banking Service Quality on Perceived Risk and Behavioral Intent, EABR & ETLC Conference
Proceedings, Dublin, Ireland, p. 266.
Ma, Z., Ma, L., Heilongjiang, and Zhao, J. (2011), Evidence on E-Banking Quality in the China Commercial Bank Sector, Global Journal of Business Research, Vol. 5. No. 1, pp. 73-83.
McKinney, V., Yoon, K., Zahedi, F.M. (2002), The Measurement of Web-Customer Satisfaction: An Expectation and Disconfirmation Approach, Information Systems
Research, Vol. 13, No. 3, Sept., pp. 296-315.
Norman, G. (2010), Likert scales, levels of measurement and the “laws” of statistics, Adv.
Parasuraman, A., Zeithaml, V. A. and Berry, L. L. (1985), A Conceptual Model of Service Quality and Its Implications for Future Research, Journal of Marketing, Vol. 49, Fall, pp. 41-50.
Parasuraman, A., Zeithaml, V.A. and Malhotra, A. (2005), “E-S-QUAL: a multiple-item scale for assessing electronic service quality”, Journal of Service Research, Vol. 7, No. 3, pp. 1-21.
Parasuraman, A; Berry, L.L. and Zeithaml, V.A. (1991), Refinement and Reassessment of the SERVQUAL Scale, Journal of Retailing, Vol. 67, No. 4, Winter, pp. 420-450.
Swaid, S.I. and Wigand, R.T. (2009), Measuring the Quality of E-Service: Scale Development and Initial Validation, Journal of Electronic Commerce Research, Vol. 10, No. 1, pp. 13-28.
Wang, YS. (2008), Assessing e-commerce systems success: a respecification and validation of the DeLone and McLean model of IS success, Information Systems
Journal, 18, pp. 529-557.
Wolfinbarger, M. and Gilly, M.C. (2001), Shopping Online for Freedom, Control, and Fun,
California Management Review, Vol. 43, No. 2, Winter, pp. 34-55.
Wong, D.H., Loh, C., Yap K.B. and Bak, R. (2009), To Trust or Not to Trust: the Consumer’s Dilemma with E-Banking, Journal of Internet Business. Issue 6, pp. 1-27.
Yaya, L.H.P., Marimon, F. and Casadesus, M. (2011), Customer’s loyalty and perception of ISO 9001 in online banking, Industrial Management & Data Systems, Vol. 111, No. 8, pp. 1194-1213.
Yee, B.Y. and Faziharudean (2010), Factor Affecting Customer Loyalty of Using Internet Banking in Malaysia, Journal of Electronic Banking System, Vol. 2010, pp. 1-21.
Zeng, F., Hu, Z., Chen, R. and Yang, Z. (2009), Determinants of Online Service Satisfaction and Their Impacts on Behavioral Intentions, Total Quality Management,
Appendix: Instrument of this Study
No State items Dimensions References
RELIABILITY (1)
1 Internet banking (IB) provides accurate information and
continuously recorded my financial data.
Accurate Ma et al. (2011)
2 Internet Banking performs the service correctly since
the first time of using.
Rely on Zeng et al. (2011)
3 Internet Banking transaction are always accurate Accurate Zeng et al. (2011)
4 Internet Banking delivered the process/transaction
within the time promised.
Consistence Zeng et al. (2009)
CONVENIENCE (2)
1 I think I can access IB anytime and anywhere, and
save time as compared to conventional banking.
Anywhere anytime
Ma et al. (2011)
2 Internet banking is having the time saving of not having
to go to the bank office.
Saving time Wolfinbarger & Gilly
(2001)
EFFICIENCY (3)
1 It’s quick to make transaction on my bank website. Quick
transaction
1 I feel comfortable with the changes resulting from the
upgrades of the systems.
Comfort with change
Hong et al. (2011)
2 In the upgrades, the use ob buttons, radio buttons, and
combo boxes is consistent with my understanding.
Consistency with user knowledge
Hong et al. (2011)
SERVICEABILITY (5)
1 Internet banking has search function that make easy to
find information.
Service Leelapongprasut et
al. (2005)
2 The structure of menus and buttons are attractive,
easy to find, easy to use, and functionally good.
Service Leelapongprasut et
al. (2005)
3 If there were a trouble, the banking officer gives
assistance quickly to solve the problems.
Repair Leelapongprasut et
al. (2005)
SECURITY (6)
1 I feel safe in my online transaction and secure in
providing sensitive information for my online transaction.
Feel secure Ma et al. (2011)
2 My bank communicates its security policies on its
website
Financial security
Ma et al. (2011)
PRIVACY (7)
1 I think my internet banking companies keep customers
information private and confidential.
Information safety
Ma et al. (2011)
2 I trust that my bank website protect and didn’t used the
personal information inappropriately.
1 I have confidence in the bank’s services confidence Kenova &
Jonnasson (2006); Kettinger & Lee (1997)
2 I think, internet banking has courteous in deliver
services and information.
courteous Kettinger & Lee
(1997)
3 Bank officer has knowledge about internet banking so
can give good explanation about internet banking.
Have the knowledge
Kettinger & Lee (1997)
REPUTATION (9)
good services. services (2010)
2 Internet banking has a reputation for being fair in its
relationship with its users.
1 IB website gives a personal attention for personal or
private setting.
Personal attention
Swaid & Wigand (2009)
2 IB website enables me to order the service in a way
that meets my needs.
Order specific needs
Swaid & Wigand (2009)
3 IB website understands my specific needs. Understand
specific needs.
Swaid & Wigand (2009)
CUSTOMER SERVICE (11)
1 The site provides ways to contact and advisor at the
bank
Provide the way to contact
Hamadi (2010)
2 I can communicate with someone from the bank (eq.
by e-mail) if I have problems with my account.
Interactivity Hamadi (2010)
ONLINE BANKING QUALITY
1 I believe that my internet banking service provide good
quality.
Quality perception
Ma et al. (2011)
2 The internet banking service quality matches with my
expectation.
1 I feel be satisfied with internet banking systems. System
satisfaction
Wang (2008)
2 I think that internet banking is of high quality Quality
satisfaction
Wang (2008)
3 I feel satisfied with the bank because implementing
internet banking.
Satisfaction with company
Zeng et al. (2009)
4 I feel be satisfied with internet based transaction Transaction
satisfaction
Zeng et al. (2009)
5 I am very satisfied with the internet banking service
delivered by bank.
Service satisfaction
Zeng et al. (2009)
PERCEIVED RISK
1 Exactly to use internet banking need computer cost, so
make me unwilling to use it.
Economic risk Aslam et al. (2011)
2 I think that using internet banking make extra
associated cost (eq. internet cost, etc).
Economic risk Aslam et al. (2011)
3 I fear of loss personal service and one-to-one
relationship with banker if I use internet banking.
Social risk Aslam et al. (2011)
4 I fear of incomplete transaction when I use internet
banking
Functional risk Aslam et al. (2011)
5 I think, using internet banking to make transaction is
high financial risk.
Economic risk Aslam et al. (2011)
6 I feel inconvenience of adopting new technology Psychological
risk
Aslam et al. (2011)
7 It is time taking and difficult to learn internet banking
interface
Psychological risk
Aslam et al. (2011)
INTENTION TO USE
1 I will visit internet banking website if I need banking
services.
Intention to visit
Wang (2008)
2 I will use internet banking to make baking transaction in
the future
Intention to do business
Wang (2008)
3 I will use internet banking services more frequently in
the future.
Increasing business
