Restrictive Deterrence: Impact of Warning Banner Messages on
Repeated Low-trust Software Use
Mario Silic
, Dario Silic
and Goran Oblakovic
University of St Gallen, St Gallen, Switzerland
Zagreb School of Economics and Management, Zagreb, Croatia
Luxembourgh School of Business, Luxembourgh, Luxembourgh
Keywords: Fear Appeals, Software, Compliance, Warning Banner, Deterrence.
Abstract: This research paper focuses on the warning messages that are one of the last lines of defense against
cybercriminals. The effectiveness of warnings in influencing users’ behavior when using low-trust
(potentially malicious) software has not been adequately addressed by the prior research. Using the restrictive
deterrence theory, supported by the Communication-Human Information Processing (C-HIP) Model, we
conducted an experimental study investigating the influence of warning messages on the repeated use of low-
trust software. The results suggest that the use of low-trust software could be reduced in frequency, or
completely abandoned, in the presence of warning messages, whereby security incidents could be better
mitigated and reduced. We suggest several implications for practitioners and offer some interesting theoretical
Warnings or deterrent messages are a form of
communication intended to inform users about the
potential harm or risk they may incur (Wogalter,
2006c). For instance, tobacco manufacturers inform
consumers about the health risks associated with
smoking. However, quite often, the warnings are
either ignored or have the opposite (i.e. boomerang)
effect to that of the suggested behavior change
(Bushman, 2006). In the computer world, this is
partly explained by the fact that people do not read
warnings (Egilman and Bohme, 2006) for the simple
reason that people become habituated to them. In a
recent study of user decisions ahead of the ‘SSL
warning’ risk, it was found that over 70% of users
continued through Google Chrome’s SSL warnings
but only 33% of users clicked through Mozilla
Firefox’s SSL warnings (Akhawe and Felt, 2013).
This clearly indicates that the attention paid to
warnings is very dependent on user experience and as
such may lead to different behaviors. This user
experience could be attributed to a simple design
issue as warnings can take different forms (i.e. color,
font, etc.) or different deterrent messages (i.e.
content), which may impact the user’s final decision.
Other reasons suggested by the previous research
relate to a lack of technical skills and general
knowledge about the computer system (Sheng et al.,
2009) so that users often do not understand what
‘SSL’ or similar technological terms really mean and,
consequently, do not pay attention to the displayed
warning. Another reason that is advanced is the issue
of trust in the computer system (Camp, 2006), as
users may be easily tricked, for example, by phishing
websites, without realizing that something is wrong.
Finally, no matter how effective, well-designed or
appealing the warning message is, the decision will
always be made by the end user. This is the whole
purpose of having a warning message. The nature of
the end user’s decision will depend on many different
factors. Some of these factors are inherent to the user
him/herself, while some are more related to the
warning message (Silic et al., 2015). To better
understand this interaction, the human-in-the-loop
(HITL) framework was proposed as a general model
to explain the interaction between the human and the
computer system by suggesting a systematic
approach to identify the potential causes of human
failure (Cranor, 2008). The model is based on the
Communication-Human Information Processing
(CHIP) model that describes the processing steps
undertaken by the user when confronted by the
warning message (Wogalter, 2006b).
Silic, M., Silic, D. and Oblakovic, G.
Restrictive Deterrence: Impact of Warning Banner Messages on Repeated Low-trust Software Use.
In Proceedings of the 18th International Conference on Enterprise Information Systems (ICEIS 2016) - Volume 2, pages 435-442
ISBN: 978-989-758-187-8
2016 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
Research into warning messages has shown that
successful communication about the risks and
benefits to users is possible, but only if the
appropriate design is built, taking into account initial
beliefs, message content and modality (Andrews,
2011). This communication can be influenced by the
restrictive deterrence theory, which suggests that the
frequency of repeated actions will decrease in the
presence of sanctions (Gibbs, 1975). Hence,
according to the C-HIP model, it is expected that the
user will stop his/her activities if he or she pays
attention to the warning message. Furthermore,
despite the fact that most of the research has focused
on the effectiveness of warnings in preventing the
occurrence of a risky event, no studies have
investigated the effect of warning banner messages on
the progression and duration of the user behavior.
Specifically, no prior study has examined the
relationship between warning messages and low-trust
(i.e. potentially malicious) software. Low-trust
software is any software for which the source cannot
be easily identified (e.g. software posted by an
anonymous programmer on
repository) (Silic, 2013; Silic and Back, 2015). Such
software can be malicious and can jeopardize users’
privacy (e.g. by stealing private information). Hence,
relying on the restrictive deterrence and supported by
the Communication-Human Information Processing
(C-HIP) model, we explore the impact of warning
messages on the duration and progression of low-trust
software use by measuring user behaviors and
decisions. Next, we describe our theoretical
background, after which we present the results,
followed by the discussion and finally, the
2.1 Communication-Human
Information Processing (C-HIP)
Research into warning messages has been categorized
into the Communication-Human Information
Processing (C-HIP) model (Conzola and Wogalter,
2001; Wogalter, 2006a). The C-HIP model posits that
in order to communicate the warning (message),
several factors have to be considered: the source, the
channel, and multiple aspects of the receiver. The
entire communication process starts with attention
and is followed by comprehension, attitudes, beliefs
and motivation. The source of the warning message
transmits the presence of a certain hazard through a
channel (Chen et al., 2014). It might be that users do
not pay attention to icons that represent SSL warnings
(Grier et al., 2008) because the channel (warning
banner message) is either inefficient in transmitting
the risk, or it might be that the user has necessary
skills to understand the risky situation and simply
ignores the warning. In this research, we focus on the
channel and the attention aspects, which are one of
the most important factors in shaping the user’s
behavior. The channel is the warning banner message
that is displayed to users, informing them about the
risks they may incur if they continue with their
actions. For instance, if the user does not pay attention
to the warning message then all the subsequent steps
(e.g. comprehension) will be ineffective. The user’s
attention can often be gained through simple visual
aspects (e.g. size, colors, graphics) (Laughery and
Wogalter, 2006). Hence, for the communication to be
attractive, a warning has to be conspicuous or salient
relative to its context (Sanders and McCormick,
2.2 Restrictive Deterrence
Deterrence theory originates from the Criminology
field and proposes that individuals who intend to
commit a crime or an antisocial act can be dissuaded
if sanctions and disincentives that are relevant to
these acts are implemented (Straub and Welke, 1998).
In the organizational context, if an employee violates
information security policies, there is a high
probability that he or she may be fired as a
consequence of his or her acts. Overall, the deterrence
theory posits that there is a high likelihood of being
caught and punished severely. Recently,
contemporary theoreticians proposed the ‘restrictive
deterrence’ model which represents the process
whereby offenders limit the frequency and severity of
their individual offending (Gibbs, 1975; Jacobs,
2010). According to Gibbs (1975), restrictive
deterrence is “the curtailment of a certain type of
criminal activity by an individual during some period
because in whole or in part the curtailment is
perceived by the individual as reducing the risk that
someone will be punished as a response to the
activity” (1975: 33). Few studies have examined the
restrictive deterrence aspect and its impact on
deterring the user from committing risky or illegal
actions (Maimon et al., 2014). When it comes to the
low-trust software context, the offender is the user
him/herself. Indeed, the user has the choice, when
confronted with the warning banner message, of
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
whether to continue or to stop his or her action. By
continuing, the user makes a conscious decision,
having been informed about the possible sanctions
that he or she may incur. However, this is true only if
the user has paid attention to the context displayed by
the warning message. In such a case, according to the
restrictive deterrence theory, it is expected that the
user will reduce the frequency of his/her acts as the
user will be sanctioned at some point in time.
Past studies have investigated the restrictive
deterrent concept mostly through qualitative research
methods (e.g. Jacobs, 1996; Jacobs, 2010; Gallupe et
al., 2011; Jacobs and Cherbonneau, 2014),
investigating relatively small samples (e.g.
Beauregard and Bouchard, 2010). One important
reason for this lack of quantitative studies could be
access to data, as not only is it difficult to conduct a
study that deals with the malware context, but there is
also the problem of how to avoid bias by not
recruiting participants directly. Overall, empirical
investigations into the restrictive deterrent concept
are still relatively scarce.
2.3 Research Hypothesis
The main objective of the warning message is to
capture users’ attention and convey information about
the possible hazard (Bravo-Lillo et al., 2013).
Consequently, according to C-HIP, in this
communication delivery process, if a user’s attention
is switched to the warning message, we can expect to
see increased compliance and better decision making.
However, users tend to easily ignore the warnings.
This is because they usually have more trust and
confidence when using high-reputation websites
(Sunshine et al., 2009). This means that users may
have higher levels of trust when downloading
software from well-known and established websites
such as In that context, users may
pay less attention to the underlying risks and may be
more willing to ignore possible risk consequences.
Hence, supported by the restrictive deterrence model
(Gibbs, 1975), we argue that when the user is
repeatedly using the software, the user may pay more
attention to a warning message that is displayed to
inform him/her about a possible hazard.
Consequently, this may lead to decreased software
use and, consequently, to abandonment. Therefore,
we hypothesize that:
Repeated software use will decrease and will lead to
abandonment in the presence of the warning banner
2.4 Study Methodology
2.4.1 Participants
We did not recruit any participants for the study,
which increases the study’s validity. In such a way,
we were able to create and simulate a genuine
environment. Institutional Review Board (IRB)
approval was given to the data collection and human-
subject protocols were followed. In addition, each
participant had to provide his or her consent to taking
part in a research study. Once the application was
started, a dialog box opened, informing the user about
the study’s objectives (and informing them that no
identifiable information would be collected) as well
as asking them to confirm their participation. If users
chose not to participate, then we did not measure any
of their activities (this was set programmatically).
Hence, users were fully aware of the experiment.
Also, all participants had to accept the end user
license agreement (EULA) which, among other
clauses, stipulated that “By downloading this
software, you consent to send usage information for
research purposes”.
2.4.2 Research Design
We used the experiment method to explore and
measure the progression, frequency, and duration (i.e.
time) of user behavior when confronted by a warning
banner message. The installation and consequent use
of the software was operationalized as an event that
had a certain duration (start and end). To measure and
operationalize these events we created a randomized
experiment using a application that was
created to support our study. The application was
fully functional software providing PDF
manipulation possibilities to the end user. To conduct
our study, we used the open-source software
repository, where the final version of
the application was published. Upon the launch of the
application (a dialog box asking for the user’s content
appeared prior to the application launch), and after
clicking on the ‘START’ button, the application
randomly displayed either a control message or the
warning banner message. Figure 1 shows the design
of the warning message. For the warning message we
used the McAfee warning design, which is one of the
most commonly used forms of AntiVirus software
and as such provided a genuine environment. Next,
we measured the events, timing the start and the
termination of each instance of software use. This
allowed us to measure the event from its start
(clicking on the ‘START’ button) till its end (clicking
Restrictive Deterrence: Impact of Warning Banner Messages on Repeated Low-trust Software Use
on either the ‘Exit’ or ‘Continue’ button displayed in
the warning message). This was operationalized by
two dependent measures: action cessation (0 meant
that the user had stopped the software use by clicking
on Exit, while 1 indicated that the user had continued
his/her software use, ignoring the warning message)
and action duration represented a continuous
measurement that counted the elapsed time (in
milliseconds) between the start and the end of the
event. The entire data collection process was fully
anonymous and invisible to the user.
Figure 1: Different warning messages.
2.4.3 Method used for Analysis
To analyze the effects we used the Kaplan-Meier
Survival Curve, which is an estimator used to
estimate survival time from the lifetime data. It is
very commonly used for medical purposes to estimate
and measure the fraction of patients surviving after
receiving treatment. The Kaplan-Meier Curve is a
popular method when it comes to analyzing different
survival times (times-to-event). Overall, the Kaplan-
Meier method is a nonparametric method used to
estimate the probability of survival past given time
points (i.e. it calculates a survival distribution)
(Kaplan and Meier, 1958). Time to event represents
an event course duration for each user, having a
beginning and an end. In this type of analysis, each
participant is characterized by three variables: 1) the
duration; 2) the status at the end of the event (exit or
continue); and 3) the warning type.
In total, 1250 events were recorded. In Table 1 a
detailed overview of the warnings displayed and the
corresponding user actions can be found. Exit action
was chosen in 36% of all cases, while 64% of users
decided to continue with the software use. When it
comes to the warning types, as expected, for the ‘No
warning’ message few users (10%) stopped using the
application, while the vast majority (90%) continued.
For the warning message, 63% of users found the
message to be rather persuasive and thus, decided to
exit the software use, compared to 37% of users who
Table 1: Overview of display warnings and users’ actions.
Exit action Continue
(decision=0) (decision=1)
Warning 590 (63%) 348 (37%) 938
31 (10%) 281 (90%) 312
621 (36%) 629 (64%) 1250
Furthermore, to understand whether warning
messages influence the time until termination, we
used the survival function – the Kaplan-Meier
Survival Curve (Figure 2). The results of this analysis
clearly show that across all event points in the
presence of the warning message, the survival time is
much shorter. Specifically, this means that in the
presence of the warning message, the duration of the
low-trust software use is very much shortened and the
message leads to a faster use termination.
Having inspected the cumulative survival plot
time, we made an initial assumption regarding how
users behave when confronted with a warning banner
message. In order to understand the typical time
duration until users stop their software use, and
consequently reduce the frequency, we will look at
the means and the medians for survival times (Table
3). Table 3 displays the mean and median survival
times, and associated statistics, for each of our
intervention groups.
We can see from the results in Table 3 that the
median survival time for the warning message is 5055
milliseconds with 95% confidence intervals from
3104.36 to 7005.640 milliseconds.
As the median is calculated as the time at which
the cumulative survival proportion is 0.50 or less (i.e.
50% or less) for the warning message, this indicates
that users need more time to make a decision and their
decision making process is affected by the warning
message content. Finally, in order to understand the
differences between the first observed events (first
software use) and all of the following software uses
by the user, we used the Cox proportional-hazard
regression model. The Cox model allows the
investigation of the relationship between the survival
of the event and independent measures (Box-
Steffensmeier and Jones, 2004).
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
Figure 2: Kaplan-Meier Survival Curve: warning vs no
warning impact.
In other words, it will provide evidence of
whether the event duration, progression and repeated
software use are decreased in the presence of the
warning banner message.
The results, calculated using IBM SPSS software,
are presented in Table 2. We can see that for the first
observed events (first software use) the hazard ratio
is 1.28 times lower than for the second observed event
hazard ratio (second use). Also, the hazard ratio for
the first observed events is more than 3 times greater
than for all incidents. Clearly, this indicates that the
hazard ratio estimate of the warning measure shows
that the warning message significantly increases the
rate of the second observed event compared to the
first observed event. This leads to much shorter event
duration and confirms that the frequency of the
repeated software use is decreased in the presence of
the warning banner message.
Our study aimed to answer the question of whether
repeated software use would be decreased, leading to
abandonment, in the presence of the warning banner
message. By conducting an online experiment
involving anonymous participants, we found that
repeated software use will be decreased in the
presence of the warning banner message.
To the best of our knowledge, this is one of the
first studies that goes beyond the initial understanding
of the user’s binary decision making process
(continue or exit), as we tried to understand whether
the warning banner message has any effect on
repeated software use. In other words, this study
aimed to determine whether, ultimately, the warning
Table 2: Cox proportional hazards survival regression
0.75* 2.45 -170.11
0.45* 3.15 -190.56
0.181* 0.653 -1238.125
p < .05 (two-tailed);
banner message, based on restrictive deterrence
theoretical assumptions, leads to decreased software
use and consequently to abandonment. Although
prior research has produced often inconclusive and
mixed results about the effectiveness of warning
messages in deterring and preventing the occurrence
of criminal incidents (e.g. it was found that warnings
are effective in deterring some illegal behavior such
as the claim padding of insured persons (Blais and
Bacher, 2007) but ineffective in deterring prostitution
(Lowman, 1992)), our study clearly shows that
warning messages have a high impact on the user’s
decisions. Consequently, warning communication
impacts the user’s behavior. This is particular
interesting as the human aspect was identified as
being the weakest link in the entire information
security ecosystem. In the current cybercrime era, a
significant number of criminal acts are committed,
facilitated or enabled (voluntarily or otherwise)
thanks to the involvement of the human factor. It is
evident that users will not cease their illegal behaviors
in the near future as there will always be factors that
will be either difficult to tackle (e.g. users’
technological skills) or difficult to influence and
change (e.g. human decision making).
However, a better understanding of how humans
behave throughout the event duration may lead the
way to a better understanding of the effectiveness of
the existing measures. Clearly, our study shows that
in the presence of the warning banner message user
behaviors are influenced and changed.
4.1 Theoretical Implications
Our study offers some interesting theoretical
implications. We used restrictive deterrence theory,
supported by the C-HIP model, and found that
restrictive deterrence can be rather efficient in
Restrictive Deterrence: Impact of Warning Banner Messages on Repeated Low-trust Software Use
Table 3: Mean and median survival times.
explaining the user’s decision making process and the
sanctions or hazards that the user may incur, in the
case of non-compliance. This is an interesting insight
as it provides some new directions for the research.
Indeed, past studies have mostly focused on the
binary decision making process and have tried to
simply understand the ‘yes’ or ‘no’ without further
understanding what happens if the user chooses yes –
could there be any abandonment and if so, when does
it occur? In this context, restrictive deterrence
becomes particularly interesting. By applying it to our
unique context, we see that the user’s conscious
decision making process is ultimately affected by the
threat of sanctions, and the applying it to our unique
context, we see that the user’s conscious decision
making process is frequency of repeated use is
4.2 Implications for Practice
This study raises some interesting implications.
Firstly, we believe that the question of the human
factor in the cybercrime area could be much better
tackled if we design more convincing warnings about
the risks that users may incur. We argue that most of
the existing communication is rather inefficient as it
mainly focuses on the legal or compliance aspects.
Instead, communication that is built around the direct
consequences for the user him/herself may prove to
be much more effective and persuasive. In today’s
world users are constantly warned about risks and
harm that may befall them, but this does not seem to
be sufficiently effective. For instance, a recent study
on the effect of warnings on decreasing cigarette
smoking showed that the standard warning messages
(e.g. smoking can kill you) are ineffective (Peters et
al., 2014). Instead, the study found that when smokers
are informed of the fact that someone they care about
may be seriously impacted by their smoking (e.g. the
health of your children could be seriously impacted)
a positive change in their behavior was observed with
a much higher percentage of people stopping
smoking. Hence, we recommend rethinking the way
in which we communicate warnings to users when
using software that has, by default, a lower reputation.
One such example is when the user is installing an
unverified driver, the Windows operating system
displays a warning message informing the user that
Windows cannot verify the publisher of the driver
software. The displayed warning message proposes
two options: 1) Not to install the driver and go to the
manufacturer’s website to check for the latest version
of the driver; or 2) Install the driver – in this option
the user is warned to install drivers only from verified
sources as unverified software may harm their
computer. However, we argue that this is a
completely ineffective means of communicating the
potential risk. Surely, for a user with advanced
technical skills and knowledge, this warning message
is probably sufficient, but that may not be the case for
an average computer system user who is not even sure
what a driver is or what kind of place and importance
it has once installed in the heart of the operating
system. Transforming the standard warning message
to a warning message that highlights the risk in a
much more direct way for the average user is not an
easy task, as it would require a new design thinking
approach that could be adaptable to various contexts.
However, by following an approach whereby, instead
of communicating the fact that ‘Windows can’t verify
the publisher’ we would for instance communicate
the message that ‘Installing this driver could
DELETE all of your data and damage your hard disk’,
an existing vicious circle between the cybercriminals,
the human factor and targets could be better
addressed to decrease and mitigate the risk. Indeed,
cybercriminals are exploiting the vulnerable human
factor as people rely on technology to inform them
and provide input for their decision making process
when confronted by a risk situation in which they
have to make a choice. Also, by increasing the quality
of the input we communicate to the user through the
warning, we would certainly decrease the number of
potential targets for cybercriminals. This
Means and Medians for Survival Time
Estimate Std. Error 95% Confidence
Estimate Std. Error 95% Confidence
Upper Bound Lower
No Warning
7111.4 2404.1 2399.3 11823.4 3282.0 153.0 2981.9 3582.0
18282.7 3509.5 11404.0 25161.4 5055.0 995.2 3104.3 7005.6
14896.1 2545.8 9906.3 19886.0 3382.0 250.7 2890.5 3873.4
a. Estimation is limited to the longest survival time if it is censored.
ICEIS 2016 - 18th International Conference on Enterprise Information Systems
communication enhancement could be applicable to
almost any area. For example, in phishing attacks via
email, the warning message could be displayed by the
operating system when certain patterns are detected
in the email content. Obviously, one drawback to this
approach is that the number of warning messages
could significantly increase, and finding the right
balance could be another challenge to face.
Ultimately, we do not want to sacrifice security to
jeopardize the user’s experience.
4.3 Limitations and Future Research
Our study has several limitations. Firstly, we were
unable to identify the users who downloaded the
application. This limits our findings as a better
understanding of who they are, their technical skills,
experience, etc. could bring more precision to the
results. We suggest that future studies might
incorporate this aspect and attempt to understand how
people’s background, cultural aspects, etc. affect their
overall software use. Secondly, we used an online
open-source repository to place our application.
While many of these repositories are labeled as
trusted, often they are limited to medium to advanced
users, as novice users do not have sufficient technical
skills to use these websites. This could have some
limitations in terms of the results as it could be
expected that novice users would be more inclined to
abandon their software use immediately rather than
continuing. Overall, we suggest that future studies
should build on the restrictive deterrence theory and
use the C-HIP model to further theorize how different
aspects of the C-HIP model interact with the
restrictive deterrence premises. It could be
interesting, for instance, to understand how attention
and comprehension are related to the frequency of
repeated software use.
The effectiveness of warnings in influencing users’
behaviors when using low-trust (potentially
malicious) software has not been adequately
addressed by prior research. This study represents a
first attempt to illustrate the way in which warnings
can reduce the frequency and the duration of low-trust
software use. These results are particularly interesting
for IT managers as they suggest that the use of non-
approved software could be reduced in frequency, or
completely abandoned, in the presence of warning
messages, so that security incidents could be better
mitigated and reduced.
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ICEIS 2016 - 18th International Conference on Enterprise Information Systems