The Impact of Privacy Concerns on Mobile
Banking: Adaptation of the IS Continuance
Model
Natalie Gerhart
University of North Texas
[email protected]
Vess Johnson
University of North Texas
[email protected]
Russell Torres
University of North Texas
[email protected]
Abstract
Recently, mobile banking has become significantly more commonplace. This is likely partially due
to the demand of young consumers for easy, efficient, and convenient access to information.
However, with mobile banking use, come some risks. We apply the IS Continuance model
(Bhattacherjee, 2001b) with perceived privacy risk to better understand the impact of privacy risk
on mobile banking continuance intention. Consistent with prior studies on IS continuance, we find
that confirmation positively influences perceived usefulness and both perceived usefulness and
confirmation positively influence satisfaction. Continuance intention also positively influences
satisfaction. However, although we find support for perceived privacy risks negatively influencing
both satisfaction and perceived usefulness, we find no support for a perceived privacy risk directly
influencing continuance intention. This indicates although privacy concerns may indeed have a
negative impact on factors influencing continuance, currently this concern does not seem to
directly influence continued usage of mobile banking.
Introduction
Historically, banking has been a face-to-face service industry in which customers walk into a physical
branch, wait in line, deposit or withdraw money, and leave to continue on with other errands. As the
industry evolved, Automatic Teller Machines (ATMs) became available to allow basic functionality after
hours and without human interaction, ultimately providing more convenience for customers. This
evolved into internet banking, which allowed people to perform many banking tasks online from the
comfort of their home or office anytime of the day, further improving the convenience of banking. Today
mobile banking, the ability to carry out banking transactions on a mobile device, is quickly surpassing
internet banking in popularity (Polverari, 2013). Using research related to internet banking can offer
insights into how we might better understand issues associated with the adoption and subsequent
continuing usage of mobile banking (e.g., Furnell 2004; Gerrard et al. 2006).
Recently, smartphones, in conjunction with mobile banking applications, have become a popular way to
manage personal and business finances. Smartphones are not only used to make phone calls, but also to
send emails, check social networking sites, take pictures, and make purchases. According to the Pew
Research Center, over half of all Americans now have smartphones (Smith, 2013) and people use their
telephone to perform multiple complex tasks. As a result, banks have begun to offer a wide variety of
services via mobile banking including account inquiries, the transfer of funds and the ability to make
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Gerhart, Johnson, Torres
check deposits using the camera of the smartphone. It is estimated that in 2013 banks will increase
spending on mobile banking by over 6% (“Follow the Money: Banks to Spend More on Mobile,” 2013).
The rapid advancement of technology and expansion of services has also resulted in a change in the
business model of banks. Competitors have arisen as online-only banks, offering no traditional physical
branches to customers, thus further encouraging the shift to mobile banking. Banks such as Ally Bank,
Capital One 360, PerkStreet, Connexus Credit Union and Bank of Internet USA avoid the cost associated
with typical brick-and-mortar banks and offer customers complete banking services with reduced fees
(Griffith, 2011).
However, considering the banking industry deals with highly sensitive information, it is important to
understand mobile banking from the customer’s perspective. In this study, we are concerned with
understanding how perceived privacy risk impacts continuance intention related to mobile banking. In
order to do this we have added perceived privacy risk to the Bhattacherjee’s IS continuance model (2001b)
in order to explore the impact it has on continuance intention, satisfaction and perceived usefulness.
The remainder of this paper is structured as follows. First, we outline our theoretical foundation and
hypotheses development. We then outline the method used in the study. Finally, we will discuss the
results of the study and explain our findings.
Theoretical Foundation and Hypotheses Development
In order to study the impact of perceived privacy risk on the users desire to continue using mobile
banking services, we have chosen to utilize Bhattacherjee’s (2001b) IS continuance model as our
theoretical lens (Figure 1). We have extended Bhattacherjee’s model by adding a construct for perceived
privacy risk.
Perceived
Usefulness
H6(-)
H4(+)
H3(+)
H5(+)
Confirmation
H2(+)
Satisfaction
H1(+)
Continuance
IS Continuance
Intention
H7(-)
H8(-)
Perceived
Perceived Security
Privacy
Risk
Figure 1: Research Model
Mobile Banking versus Internet Banking
Mobile banking has grown in popularity in recent years. In many ways, mobile banking represents a
natural evolution of Internet banking. What sets mobile banking apart from internet banking is that the
functionality is available through an application on a mobile device. Many banks offer some sort of
functionality through applications on mobile devices. Mobile banking adoption has been well researched
over recent years, many using theoretical lenses such as technology acceptance model (TAM) (Davis,
1989), diffusion of innovation theory (DOI) (Rogers, 1995) and task technology fit (TTF) (Goodhue &
Thompson, 1995).
Gu et al (2009) expanded TAM to include factors such as social influence, system quality familiarity with
the bank and situational normality to study the intention to adopt mobile banking technology. KoenigLewis (2010) combined TAM and DOI to explore the impact of credibility, trust and risk on mobile
banking adoption. Zhou et al (2010) used a combination of UTAUT (Venkatesh et al. 2003) and TTF to
explore mobile banking adoption. In addition multiple studies have looked at factors that impact
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Editors: Gurpreet Dhillon and Spyridon Samonas
The Impact of Privacy Concerns on Mobile Banking: Adaptation of the IS Continuance Model
adoption of mobile banking services (e.g., Calisir and Gumussoy 2008; Gu et al. 2009; Koenig-Lewis et al.
2010; Laukkanen and Kiviniemi 2010; Zhou et al. 2010), but there has been little research on continuance
intention with respect to mobile banking. Further, the impact of perceived privacy risk on the
continuance decision process has been largely unexplored.
IS Continuance
Given that current usage of mobile banking is rapidly exceeding online banking (Polverari, 2013) together
with the vast body of work concerning mobile banking adoption, focus should shift from adoption, to
better understand what causes continued use of mobile banking. The IS Continuance model, based on
expectation confirmation theory (ECT) (Oliver, 1977, 1980), maintains that meeting or failing to meet
expectations impacts perceived usefulness and satisfaction (Bhattacherjee, 2001a, 2001b). If expectations
are met or exceeded, then perceptions of usefulness and satisfaction are positively influenced. On the
other hand, if expectations are not met, then perceptions of usefulness and satisfaction are negatively
influenced. Perceived satisfaction then influences continuance intention. This model is important in
understanding post-adoption continuance. This model has previously been used to understand continued
use in multiple e-commerce and banking research studies (Eriksson & Nilsson, 2007; Hung, Yang, &
Hsieh, 2012; V. Johnson, Torres, Phillips, & Rahnamaee, 2013; Kim & Oh, 2011; Zhao, Lu, Zhang, & Chau,
2012), and thus is relevant and appropriate for this study context.
Continuance intention is simply the idea of intent to continue using a product or service. Satisfaction can
be defined as a users’ feelings about use of mobile banking (Bhattacherjee, 2001b). Satisfaction is
specifically post-adoption satisfaction, meaning that attitudes can change after a user has experienced the
service (Bhattacherjee, 2001b; Thong, Hong, & Tam, 2006). Marketing research shows that when
expectations are satisfied, intent can also be increased (Boulding & Kalra, 1993). Similarly, satisfaction
literature shows that satisfaction leads to intent in a service context (Cronin, Brady, & Hult, 2000). Given
this logic, we hypothesize:
H1: Satisfaction positively influences continuance intention.
Secondly, it is important to understand what factors have an impact on satisfaction. Confirmation is the
mental equation balancing a users’ expectations with actual results (Bhattacherjee, 2001b). Confirmation
is therefore a cognitive process that results in a decision such as satisfaction or dissatisfaction and has
been well established in previous theories (Ajzen, 1991; Bhattacherjee, 2001b). In a similar regard,
perceived usefulness, from TAM (Davis, 1989), is related to expected benefits. Therefore, it follows that if
mobile banking is perceived as useful, it will be more satisfying. If something has a use, it meets a need or
a want and satisfaction implies that the need or want has been fulfilled. Considering these arguments, we
suggest the following antecedents to satisfaction:
H2: Confirmation positively influences satisfaction.
H3: Perceived Usefulness positively influences satisfaction.
Perceived usefulness should have a clear impact on continuance as well (Bhattacherjee, 2001b). In TAM,
perceived usefulness positively related to behavioral intent (Davis, 1989). In this model, IS continuance is
the intent to continue using a service, the only difference being that it is not the first use and implies
multiple future usages (Bhattacherjee, 2001b). This relationship has been supported in models using
TAM (Davis, 1989; Gu et al., 2009; Luarn & Lin, 2005), as well as research using the IS continuance
model (Bhattacherjee, 2001a, 2001b; Thong et al., 2006). It has also been operationalized as performance
expectancy in the UTAUT model (Venkatesh et al. 2003). Given this, we hypothesize:
H4: Perceived Usefulness positively influences continuance intention.
As shown earlier, confirmation is a cognitive balancing between expectations and the result of actual
usage. Therefore, confirmation will be related to perceived usefulness in a cyclical way (Bhattacherjee,
2001b). This stems from a cognitive bias in psychology that suggests that people will look for more
justification of purchases after the purchase is complete (Cohen & Goldberg, 1970). In this context,
because this is a post-adoption model, once a person has committed to use mobile banking, he or she is
likely to readjust thoughts about how useful mobile banking is (Bhattacherjee, 2001b). Essentially, the
user will mentally confirm mobile banking use. Therefore, we propose the following hypothesis:
Information Institute Conferences, Las Vegas, NV, May 21-23, 2014
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Gerhart, Johnson, Torres
H5: Confirmation positively influences perceived usefulness.
Perceived Privacy Risk
There are two obvious features of mobile banking that give it specific cause for attention: it is delivered
on a mobile device through a mobile application, and it involves the transmission of valuable personal
information. Mobile banking, by definition, is performed on a cell phone or other mobile device (Zhou et
al., 2010). This requires information to be sent from the application over a network, which may be
vulnerable to external threats. Such behavior could be perceived as risky to the user in much the same
way that similar transactions are perceived over the Internet (Malhotra, Kim, & Agarwal, 2004). In
addition, any transaction involving money has been found to cause more concern for security (Furnell,
2004), People generally consider financial information to be highly important, and as a result, have
heightened concern for privacy and security. This manifests itself as perceived privacy risk, which has
also been referred to as mobile banking risk, and can be defined as perceived threat of loss through
uncertainty of technology use (Featherman & Pavlou, 2003).
Perceived usefulness is “the cognitive appraisal of how the IS will help the user to fulfill a task” (Wang et
al. 2009, p. 312). If a person perceives a service as more risky, it will logically appear to be less useful. It
has been shown that perceived risk can have a negative impact on performance expectancy (Luo, Li,
Zhang, & Shim, 2010). Expectancy in the continuance model is operationalized as perceived usefulness
because it is a moment in time after the adoption has occurred (Bhattacherjee, 2001a). It is this logic and
previous research that leads us to hypothesize:
H6: Perceived privacy risk negatively influences perceived usefulness.
Satisfaction is a psychological state which reflects the discrepancy between expectations and performance
(Bhattacherjee 2001a). In the context of the present study, satisfaction reflects the affective state that
results from the cognitive appraisal of expected versus actual value of mobile banking use. Perceived
privacy risk, whether justified by an actual loss of data or not, is likely to undermine the level of
satisfaction experienced by a mobile banking user as concern for the security of financial information
could negatively impact the emotions associated with the use of that service. This is consistent with
research that has found that perceived risks have a negative impact on satisfaction in shopping
experiences (M. S. Johnson, Sivadas, & Garbarino, 2008; Yüksel & Yüksel, 2007). As both deal with
sensitive financial information, extending consumer shopping research to the mobile banking context is
justified. Thus, the relationship between perceived privacy risk and satisfaction is hypothesized as
follows:
H7: Perceived privacy risk negatively influences satisfaction.
The effect of perceived privacy risk will also likely have a direct negative effect on continuance intention.
A consumer that perceives mobile banking as useful and is, in all other ways, satisfied with mobile
banking, might ultimately choose to avoid the behavior due to a perceived threat of loss. Others have
shown that risk perception can ultimately change intent (Jarvenpaa, Tractinsky, & Saarinen, 1999). Those
who feel a threat of security breach will be less interested in continuing to use mobile banking (Luo et al.,
2010). Considering this, we hypothesize:
H8: Perceived privacy risk negatively influences continuance intention.
Method
Our research team tested the proposed model by surveying students at a large university in the
southwestern United States. Students are an appropriate sample for this type of study, as they are highly
likely to use mobile technology. 79% of 18-24 year-olds now own a smartphone, and thus would be able to
perform online banking tasks through applications (Smith, 2013). Additionally, younger individuals are
likely to adopt Internet banking (Calisir & Gumussoy, 2008), making the extension into mobile banking a
logical next step for student populations.
The survey was offered in both online and paper versions. Some respondents received extra course credit
for participation in our survey, which we hoped would increase focus and attention to the survey. A nonresearch extra credit task was offered any individual who chose not to participate in the survey.
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Editors: Gurpreet Dhillon and Spyridon Samonas
The Impact of Privacy Concerns on Mobile Banking: Adaptation of the IS Continuance Model
Overall, 232 responses were collected. 16 incomplete responses were eliminated from further analysis,
leaving 216 usable responses that were further analyzed for this study. The demographics of the
respondents can be found in Table 1. As expected, over 91% of the respondents were 29 or younger, thus
meeting the goal of sampling a relatively young population whose members are more likely to have
adopted mobile banking. Further, 85.7% responded that they do, indeed, use mobile banking at least
once per week. This is an important factor as we are trying to better understand continuance, not initial
adoption. Also of note, 78.7% of the respondents indicated that they had never experienced information
loss of any kind, and thus, might be less fearful of the threat.
Table 1: Demographics
Male
Female
Yes
No
Gender
144
71
66.7%
32.9%
Experienced Loss
45
170
20.8%
78.7%
18-21
22-25
26-29
30-33
34-37
38-41
42-45
>45
Age
106
71
20
6
1
4
4
4
49.1%
32.9%
9.3%
2.8%
0.5%
1.9%
1.9%
1.9%
0
1
2
3
4
5
>5
Uses per Week
31
28
47
31
26
16
37
14.4%
13.0%
21.8%
14.4%
12.0%
7.4%
17.1%
Previously validated scales were adapted to measure each study construct. The confirmation, satisfaction,
and continuation scales were adapted from the original IS Continuance framework (Bhattacherjee,
2001b). Perceived usefulness measures were adapted from TAM (Davis, 1989). Perceived privacy risk
measures were adapted from an internet banking study (Lee, 2009). Each question was provided with a
6-point Likert scale ranging from Strongly Disagree (1) to Strongly Agree (6). See Appendix A for a
complete listing of the measures used.
Analysis and Results
The data were analyzed using SmartPLS (Ringle, Wende, & Will, 2005), a Partial Least Squares (PLS)
Structural Equation Modeling (SEM) solution. The analysis was conducted in two steps. First, we
assessed the measurement model to confirm the reliability and validity of the constructs. Second, the
structural model was assessed in order to evaluate the model’s structural relationships.
A summary of final measurement model assessment findings is presented in Table 2. We followed the
guidelines specified by Hair, Ringle, and Sarstedt (2011) for assessing and reporting measurement model
findings.
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Gerhart, Johnson, Torres
Table 2: Measurement Model Summary
Item
Mean
Std.
Dev
Factor
Loading
PU1
5.11
1.13
0.87
PU2
4.68
1.22
0.90
PU3
4.84
1.16
0.94
PU4
5.00
1.05
0.89
PU5
4.79
1.16
0.87
SA1
4.86
1.26
0.95
SA2
4.84
1.21
0.95
SA3
4.77
1.26
0.94
SA4
4.63
1.27
0.95
SA5
1.28
0.91
CI1
4.52
4.93
1.48
0.93
CI3
4.86
1.42
0.96
CI4
5.05
1.27
0.94
CI5
4.94
1.38
0.96
CO1
4.56
1.28
0.93
CO2
4.41
1.30
0.94
CO3
4.75
1.29
0.92
SR1
2.99
1.53
0.91
SR2
3.38
1.56
0.92
SR3
3.67
1.57
0.87
SR4
3.25
1.49
0.94
Factor Correlations
Cronbach
α
Composite
Reliability
AVE
0.94
0.95
0.80
0.89
0.97
0.97
0.88
0.85
0.94
0.96
0.97
0.90
0.78
0.86
0.95
0.96
0.95
0.86
0.78
0.86
0.76
0.93
0.95
0.95
0.83
-0.48
-0.60
-0.49
-0.53
PU
SA
CI
CO
SR
0.91
SR5
3.08
1.51
0.91
PU=Perceived Usefulness, SA=Satisfaction, CI=Continuance Intention, CO=Confirmation, SR=Perceived Privacy
Risk
Shaded items on the diagonal are the square roots of AVE
We first evaluated the internal consistency of the measures employed in the study in order to demonstrate
reliability. Both Cronbach’s alpha and composite reliability were calculated. The lowest value for either
measure was 0.94, well in excess of the accepted minimums of 0.7 for Cronbach’s alpha (Nunnally &
Bernstein, 1994), and 0.6 for composite reliability (Henseler, Ringle, & Sinkovics, 2009; Nunnally &
Bernstein, 1994) . Additionally, indicator reliability was also assessed by ensuring that each indicator’s
absolute loading exceeds 0.7 (Hair et al., 2011). The lowest observed loading was 0.87, suggesting
adequate reliability of our measures.
Next, we evaluated convergent validity by evaluating the Average
Variance Explained (AVE). AVE values in excess of 0.5 suggest that the latent variable explains at least
half of the variance of its indicators (Henseler et al., 2009). The data that were analyzed meet this
criterion as observed AVE values are greater than or equal to 0.80. Finally, The Fornell-Larcker Test
(Fornell & Larcker, 1981) was used to assess discriminant validity. We examined the square-root of AVE
values to confirm they were greater than the inter-construct correlations. This is confirmed in our
analysis indicating good discriminant validity.
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Editors: Gurpreet Dhillon and Spyridon Samonas
The Impact of Privacy Concerns on Mobile Banking: Adaptation of the IS Continuance Model
Perceived
Usefulness
R2=68.0%
-0.111*
0.205**
0.397***
0.765***
Confirmation
0.439***
Satisfaction
R2=82.5%
0.660***
-0.190***
Continuance
Intention
R2=74.1%
-0.037
Perceived Privacy
Risk
*** Significant at 0.001
** Significant at 0.01
* Significant at 0.05
Figure 2: Structural Model
After confirming the measurement model indicated satisfactory reliability and validity of our latent
constructs, we examined the structural model. As PLS lacks the fit indices of other SEM techniques, the
structural model is primarily assessed through the examinations of R-square statistics and path
coefficients. 68.0% of the variation in perceived usefulness can be explained by the positive influence of
confirmation and the negative influence of perceived privacy risk. Perceived Usefulness, confirmation,
and perceived privacy risk collectively explain 82.5% of the variance of satisfaction. Finally, 74.1% of the
variance of IS Continuance is explained by perceived usefulness, satisfaction, and the negative impact of
perceived mobile banking risk.
All of the individual hypotheses are supported except one. H1, that satisfaction positively impacts
continuance, is supported with a path coefficient of 0.660 (p<.001). The positive relationship between
confirmation and satisfaction (H2) is supported with a path coefficient of 0.439 (p<.001). Perceived
usefulness has a positive impact on satisfaction (H3) as indicated by the path coefficient 0.397 (p<.001).
H4 suggests that perceived usefulness has a positive impact on overall continued use, which is supported
with a coefficient of 0.205 (p<0.01). Confirmation has a positive relationship with perceived usefulness
(H5) which is shown with a path coefficient of 0.765 (p<.001). H6 tests the negative impact of perceived
privacy risk on perceived usefulness, and is found to have a coefficient of -0.111 (p<.05). Perceived
privacy risk was also hypothesized to have a negative impact on satisfaction (H7), and does with a
coefficient of -0.190 (p<.001). Finally, H8 proposed the negative impact of perceived mobile banking risk
on IS Continuance. This proved to be the only insignificant relationship, with a path coefficient of -0.037,
and thus H8 is not supported. The complete structural model is shown in Figure 2 with path coefficients
and significance indicated.
Discussion
The goal of the study was to better understand factors that influence the continued usage of mobile
banking. In particular, we were interested in the impact that perceived privacy risk might have on
continuance. Seven of eight hypotheses put forth were supported (Table 3).
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Gerhart, Johnson, Torres
Table 3: Hypotheses
H1
H2
H3
H4
Satisfaction positively influences Continuance Intention
Confirmation positively influences Satisfaction
Perceived Usefulness positively influences Satisfaction
Perceived Usefulness positively influences Continuance Intention
Supported
Supported
Supported
Supported
H5
Confirmation positively influences Perceived Usefulness
Supported
H6
Perceived Privacy Risk negatively influences Perceived Usefulness
Supported
H7
Perceived Privacy Risk negatively influences Satisfaction
Supported
H8
Perceived Privacy Risk negatively influences Continuance Intention
Not Supported
For this study the IS continuance model (Bhattacherjee, 2001b) served as our theoretical lens. Consistent
with studies using this model in other technology contexts we found support for perceived usefulness
(H4) and satisfaction (H1) positively influencing continuance intention. Confirmation was found to
positively influence both satisfaction (H2) and perceived usefulness (H3). Results supported perceived
usefulness positively influencing satisfaction (H3).
New to this model was the impact of perceived risk on the intent to continue using mobile banking.
Consistent with our hypotheses we found support for perceived privacy risk negatively influencing both
perceived usefulness (H6) and satisfaction (H7). However, perceived privacy risk was insignificant with
respect to its impact on continuance intention (H8). This implies that while the privacy risk associated
with mobile banking influences the individual’s perception of usefulness and overall satisfaction with the
service, it does not directly influence their intention to continue using mobile banking in the future. This
is consistent with the notion of “privacy calculus” where one might be willing to risk some level of privacy
in order to gain something in return (Dinev & Hart, 2006).
Implications
This work advances research into mobile banking beyond initial adoption to consider continuance
intention. Bhattacherjee’s IS continuance model (2001a) has been used in several different contexts to
study technology continuance intention, but this is one of the first studies to consider continuance with
respect to the mobile banking area. In addition, we consider the impact of perceived privacy risk on
mobile banking continuance intention. Given the highly sensitive nature of the financial and personal
information used in mobile banking together with the more recently highly visible security breaches, this
research makes a very timely contribution.
From a practical perspective, understanding current customers, customer retention and customer churn
is very valuable from a marketing and business perspective. It is generally accepted that retaining
customers is easier and much more cost effective than acquiring new customers (Reichheld & Schefter,
2000). Both online banks and traditional brick-and-mortar banks moving into the mobile banking
market should be aware of the impact of their customers’ privacy concerns with respect to continuing with
the service.
Limitations and Future Research
One limitation of our study is the use of college students. It could be argued that by doing so we sacrificed
some level of generalizability of results. We do feel this convenience sample was appropriate given the
nature of the study. Students tend to be open to new technology and the vast majority of those in the 1830 age demographics are well versed in the use of smart phones and mobile technologies. This opens the
door to future research to consider continuance intention toward mobile banking in a broader
demographic, including participants from other age groups.
Secondly, because this is a cross-sectional survey, there is no way to prove causality of events. While this
might not be important to the meaning of the findings presented here, it would be informative to perform
a longitudinal study that would be able to determine causality. Similarly, by following consumers over
time there might be insight into how options and perceptions change over time, perhaps ultimately
influencing continuance intent.
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Editors: Gurpreet Dhillon and Spyridon Samonas
The Impact of Privacy Concerns on Mobile Banking: Adaptation of the IS Continuance Model
Conclusion
This work further extends mobile banking literature by looking at factors that contribute to continued
usage. Mobile banking applications have been widely adopted, and thus research should transition from
adoption to include more work on continuance. Understanding how to positively impact the users’
perception of usefulness and satisfaction related to mobile banking could be of significant value to the
banking industry.
Specifically, the significant impact of perceived risk on perceived usefulness and satisfaction is of note
both theoretically and practically. Convincing users to continue using mobile banking could result in
large cost savings for banks, increased revenues, as well as could be beneficial to the end user, who is
ultimately looking for maximum convenience and efficiency in a secure way.
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