External Contextual Factors in Information Security Behaviour
D. P. Snyman
and H. A. Kruger
School of Computer Science and Information Systems, North-West University, 11 Hoffman Street,
Potchefstroom, South Africa
{dirk.snyman, hennie.kruger}@nwu.ac.za
Keywords: Information Security Behaviour, Contextual Factors, Human Factor in Information Security.
Abstract: Human behaviour is often considered to be irrational, difficult to understand, and challenging to manage. This
phenomenon has a direct impact on the way in which humans behave when confronted with information
security which, in turn, complicates how security is to be managed. This research attempts to investigate the
role that contextual factors play in how humans behave, specifically with regards to information security.
Contextual factors are identified that influence human behaviour in general. These factors are conceptualised
in relation to existing models of behaviour and subsequently mapped to information security behaviour. A
practical research exercise, relating to information security behaviour, is conducted with a university
residence as the contextual environment. The specific contextual factors, and how they relate to information
security, are discussed. Information security behavioural threshold analysis is employed to evaluate the impact
of the identified contextual factors on the residence’s security behaviour. The results are reflected upon, based
on the results from the threshold analysis. The paper concludes by highlighting the contributions that were
made towards understanding contextual factors in information security.
The digitalisation of everyday activities is rapidly
expanding to include even the most basic day-to-day
interactions with people and (previously undigitized)
systems (Scholl, 2018). This has given rise to an
enhanced awareness and responsibility that regulators
and governments have in providing frameworks that
facilitate and prescribe the protection of the
information and privacy of individuals, for instance,
the General Data Protection Regulation in Europe.
Similarly, organisations have a heightened
responsibility and, given the regulatory frameworks,
a level of accountability in safeguarding the
information of customers and employees alike.
While these developments in information security
enhancement are noble in concept, the actualisation
thereof remains complicated. Even within the strict
demands on organisations and their adherence
thereto, one of the prevailing threats to the
information security of the end-users remains the
users themselves. One would argue that when
individuals are given the opportunity to protect their
privacy and information security interests they would
do so with due consideration. However, this is rather
the exception than the rule given the occurrence of
phenomena like the privacy paradox, i.e. the wilful
disclosure of one’s private information, even when
such disclosure is known to be ill-advised (Barth et
al., 2019). This unpredictability of the human factor
in information security remains difficult to
understand and therefore difficult to manage. In an
attempt to gain better insight into the reasons for
inconsistent and often contradictory behaviour,
information security and privacy research is often
concerned with the underlying factors that drive
behaviour when people are presented with the
abovementioned digitised interactions (Scholl, 2018).
Among the different approaches to analyse
information security behaviour, psychological
models are often employed to explain the way in
which the thought processes work that eventually lead
to a specific behaviour or course of action. Most
studies on human behaviour focus on the internal
thought processes and motivations that inform
intention and, eventually, the behaviour of a person.
Some or other psychological theory or model of
behaviour is usually employed as a guiding
framework for research in this field. A few of the
more commonly used theories include knowledge,
attitude, behaviour (KAB), the theory of reasoned
action (TRA) (Shropshire et al., 2015), protection
motivation theory (PMT) (Parsons et al., 2017), and
the theory of planned behaviour (TPB) (Parsons et al.,
2017). Such models are usually focussed on the
intrinsic processes of an individual’s cognition and
rarely (if ever) consider the setting that an individual
finds themselves in.
In contrast thereto, earlier work by Willison and
Warkentin (Willison and Warkentin, 2013) postulates
that understanding the mutual interaction of thought
processes and organisational context is important to
effectively employ information security controls in an
organisation. This is confirmed by more recent
literature (Kroenung and Eckhardt, 2015; Johnston et
al., 2019; Wu et al., 2019) which has further identified
that there is an ongoing need for approaches and
methods that bridge the gap in information security
research by both understanding the context of an
individual and, where applicable, factoring
contextual factors into any analysis that attempts to
quantify information security behaviour.
In an attempt to contribute to filling the
abovementioned gap, the aim of this research is
therefore to 1) theorise on the external factors (i.e.
external to an individual) that influence information
security behaviour, and 2) to present the application
of a model that takes context into account and predicts
information security behaviour of a group.
The remainder of the paper is structured as
follows: Section II describes factors that typically
influence human behaviour, and thereafter Section III
shows how these factors relate to information security
behaviour. In Section IV, a cursory introduction into
a model that considers contextual factors in
information security behaviour is presented along
with the findings of a real-world application thereof.
The study concludes in Section V with a reflection on
the study and a look ahead to possible future work.
In the preceding Introduction, the need to
conceptualize and understand the contextual factors
that influence information security behaviour was
highlighted. In order to eventually bring about a
discussion of these factors in terms of information
security, a discussion of contextual factors is first
presented here in general terms, i.e. factors that
influence everyday behaviour.
In a recent study, Kirova and Thanh (2019), based
on the influential earlier work of Belk (1975),
investigate the contextual factors that influence
smartphone use. They identify five common aspects
of all circumstances that can be used to identify the
influences that are exerted upon an individual. These
aspects are listed below:
Physical milieu;
Social milieu;
Perspective of elapsed (or remaining) time;
Individual predisposition; and
Individual intention.
Even though these factors are all conceptualised
as being contextual in nature and they all contribute
to the experience and environment in which
behaviour is to be actualised, for the purposes of this
paper they may be classified as being either an
intrinsic or an extrinsic factor, i.e. intrinsic or
extrinsic to an individual. The contextual factors may
be grouped as follows in Table 1.
Table 1: Categorisation of contextual factors in behaviour.
Contextual factors in behaviour
Extrinsic factors Intrinsic factors
Physical milieu
Perspective of elapsed (or
remaining) time
Social milieu Individual predisposition
Individual intention
As mentioned before, when psychological
theories and models are compared to the classification
of the contextual factors in Table 1, one commonality
may be identified. Many of these theories are centred
around the intrinsic factors, e.g. individual
predisposition (which relates to intention). Intention
is one of the core indicators which guides behaviour
in the TPB. The extrinsic factors are not explicitly
provided for in these frameworks.
Given that intrinsic factors are already considered
in these theories, this research focusses on the
extrinsic factors and how they influence one’s
behaviour. A short description is provided below for
each of the extrinsic factors from Table 1:
The physical milieu is an aspect that is derived
from the tangible environment in which an individual
finds themselves. This aspect considers the
characteristics that define the physical experience that
relate to what someone sees, feels (touch), hears,
tastes, and smells.
Social milieu refers to the influence that other
people have on an individual. They may be present in
the environment (direct influence by example) or
influence an individual through some other means
like through digital interactions. An individual may
either be formally acquainted with these influencers
(e.g. friends, co-workers, or family) or be wholly
unfamiliar (e.g. shop attendants or internet
Recall that the first aim of this paper is to theorise
on external factors in information security behaviour.
To satisfy this aim the aforementioned extrinsic
contextual factors will be contextualised in terms of
information security in the following section.
Two extrinsic contextual factors in behaviour have
been identified in the previous section namely,
physical milieu, and social milieu. In terms of
information security behaviour, it is imperative to
understand how these external factors manifest in the
environments where security behaviour is performed.
For instance, the physical milieu does not only relate
to aspects that may be observed through one’s senses,
but also relates to aspects such as access to
information, and convenience. Social milieu may also
relate to aspects of interactions with others that are
more intangible, e.g. body language, and peer
pressure. Table 2 shows some typical material
examples of the forms which these factors may adopt
in relation to information security behaviour. These
examples are presented here (and later in Table 3) as
conceptualised by the authors based on characteristic
information security behaviours and university
residence environments, and how they might relate to
the extrinsic factors as identified from the work of
Kirova and Thanh (2019). The examples listed in this
paper are by no means exhaustive and many more
may exist.
In a related study, Snyman and Kruger (2017)
investigate information security behaviour in terms of
the TPB model. What differentiates their study from
other studies that are based on the same model, is that
the study speculates on the applicability of contextual
influences on behaviour alongside the existing
intrinsic factors that the model is based on. Such
contextual influences are only described in theoretical
terms and further investigation was left for future
work. In an attempt to build on this initial
groundwork, their approach, together with the two
external contextual factors identified above, will be
used to guide the practical part of this research.
To contextualise the TPB (as applied in (Snyman
and Kruger, 2017)) with the current research
presented in this paper, a graphical depiction is
provided in Figure 1. Figure 1 shows a conceptual
diagram of the interaction between the TPB and the
external contextual factors. From the figure, it can be
seen that the external (extrinsic) factors have an
influence on the intrinsic factors. It is in the context
of the intrinsic factors that the TPB then describes
how attitude, norms and behavioural control guide the
eventual behaviour of a person. Snyman and Kruger
(2017) further argue that an approach that is able to
implicitly capture information about external factors,
and use this information in predicting eventual
behaviour, are the so-called Threshold models of
collective behaviour as envisioned by Granovetter
Table 2: General external factors that influence information security behaviour.
External factors in information security behaviour
General extrinsic factor Example of extrinsic factor that influences security behaviour
Physical milieu
Ease of access to systems, processes and people.
Level of convenience associated with certain tasks.
Availability of technical expertise.
Presence of security controls.
Social milieu
Peer pressure
Presence of co-workers/family/friends.
Organisational structure.
Required to work together with others.
Collective purpose.
Exposed to the actions/behaviours of others.
External (extrinsic) factors that
influence behaviour
Subjective norm
Perceived behavioural
Attitude towards
Intention Behaviour
Physical milieu
Social milieu
Intrinsic factors
Figure 1: Conceptual model of the influence of contextual factors in relation to the TPB.
He argues, and mathematically motivates, that
human behaviour is guided by the example that is set
by others, i.e. an extrinsic factor. A person is
presumed to always try and increase their utility
within a given situation, guided by a perceived
cost/benefit trade-off of participating in a certain
behaviour, juxtaposed by choosing not to participate.
The assumption is made that there are only two
opposing options for the behaviour, i.e. no third (or
additional) option(s) exists, and one must choose
either of the outcomes that participation or abstinence
People are assumed to be rational in their
decision-making, always favouring benefit over cost.
However, as the number of other people that perform
a specific behaviour increases, a mental shift occurs
that causes the perceived benefit to rise to a level that
exceeds the perceived cost that is associated with the
behaviour, even if the contrary was initially true. This
pivotal point in the decision-making process may be
described by an examination of the concept of
behavioural thresholds.
Granovetter (1978) hypothesises that each
individual has an intrinsic threshold for participation
in behaviour. This threshold may be expressed as the
number of other people that should first be engaging
in a behaviour before the associated benefit will
outweigh the cost in the individual’s mind. At this
point, it should be noted that when a person perceives
the benefit to outweigh the cost from their
perspective, without needing any external influence,
that they will perform a behaviour of their own
accord. Such individuals may be referred to as
instigators. They are required, especially in a high-
cost situation, to be the catalyst that influences others
to follow their example.
In order to apply this theoretical model in a real-
world situation, behavioural threshold analysis is
employed. This analysis entails that the threshold
values for participation in a behaviour is known for
each of the group members and is dependent on, and
specific to, the composition of individuals that
constitute the group. The process by which these
individual thresholds may be elicited from the group
members by means of self-reporting questionnaires is
presented in (Snyman and Kruger, 2019). Given the
individual thresholds, mathematical aggregation is
used to provide an outlook for the eventual group
To relate the threshold model and analysis of
Granovetter (1978) back to information security, a
practical exercise was conducted which is described
in the following section.
Taking inspiration from a similar exercise which was
conducted in an industry setting (Snyman et al.,
2018), a behavioural threshold analysis experiment
was conducted to examine the information security
behaviour of students at a predominantly residential
South African university. The experiment was
specifically designed with a new set of contextual
factors in mind when compared to that of (Snyman et
al., 2018). In contrast to the industry setting in
(Snyman et al., 2018), the context of the university
students is one of living together in a university
residence. A description of these specific contextual
factors, in reference to the general factors in Table 2,
is given below in terms of the physical milieu and the
social milieu.
Physical Milieu – A university residence, as
mentioned above, physically consists of common
areas (lounges, television rooms, kitchens, laundry
rooms, public computer rooms, reception), as well as
private sleeping quarters which houses one or two
students per room. The close proximity of this kind of
living arrangement provides the members of the
residence with unprecedented access to the behaviour
of others. Both in practical terms that allow the
observation of the behaviour of others, and physical
terms in which access is afforded to personal and
university computers and networks.
A certain level of convenience is conveyed by
living in close quarters. For instance, if network
access is required after business hours and a person’s
credentials have expired, it is easy to simply ask any
other inhabitant of the residence to supply their
details. It is convenient for the borrower as their
ability to access the network is instantly restored
without the need to contact the help-desk which will
not respond in real-time.
Given the combination of different academic
levels and technical proficiencies that cohabit, it is
probable that someone with a high level of know-how
or expertise can readily be found to help circumvent
security controls that stand in the way of quickly or
conveniently completing a task.
An example of such a circumvention is accessing
dubious websites that are restricted on the university
network by means of masking their network traffic by
employing virtual private networks to third party
providers. In these cursory examples, one sees that
the physical milieu provides means and opportunity
to engage in risky information security behaviour.
The social milieu, described below, may help provide
the motive.
Social Milieu – University residences are a
socially rich environment with a unique culture. This
gives rise to many interactions between people that
may influence how they behave. In information
security terms, this influence may contribute to bad
security behaviour in the following ways:
In a residence, there is a constant presence of other
people. Even in a private space like sleeping quarters,
there might be another resident present. This implies
that some actions of an individual, that would
normally go unnoticed, are being observed. If they
visit a dubious website, someone may be there to
observe it. When password sharing occurs between
two parties it may be witnessed by any or all of the
others present. Therefore, this constant presence may
convey an unprecedented sense of awareness of the
information security habits of the resident corps. The
awareness may set the precedent for future behaviour.
Peer pressure is ever-present in university
residences (Johnson et al., 2005; Young and de Klerk,
2008; de Klerk, 2013). A strict hierarchy prevails
where a pecking order distinction is made based on
the number of years someone has been residing in the
specific residence. There is also a specific distinction
between junior (usually first-year students or first-
time entrants) and senior students. In this hierarchy,
juniors have very little autonomy and, especially
during an initial orientation, are forced to obey senior
residents (de Klerk, 2013). The peer pressure and
hierarchy that is present in residences are usually seen
as factors in hazing (de Klerk, 2013) and alcohol
consumption in literature (Johnson et al., 2005;
Young and de Klerk, 2008) but is also applicable to
security behaviour. A resident may easily be coerced,
through this hierarchical structure and peer pressure,
into divulging credentials, not reporting security
circumventions, downloading illicit content, etc.
Even though the hierarchy may be seen in a
negative light as illustrated above, it may also
contribute to a sense of belonging and camaraderie
(de Klerk, 2013). There is an implied level of trust
associated with shared experiences. This is
compounded by the compulsory attendance of events
(Johnson et al., 2005; de Klerk, 2013) that are meant
to reaffirm the bond between the residents. This trust
allows for a false sense of safety where security is
concerned. For instance, one might not appropriately
scrutinise an email that was (presumably) sent by a
confidant and assume it to be safe. The assumption
will leave one open to malware and phishing attacks.
Extending Table 2, Table 3 summarises the extrinsic
factors (as described in Section II) that relate to the
context of students living together in a residence.
Table 3: External factors in student residence living that influence information security behaviour.
External factors in information security behaviour
Extrinsic factor Factors in general security behaviour Factors in student security behaviour
Physical milieu
Ease of access to systems, processes and
Level of convenience associated with certain
Availability of, and access to expertise.
Presence of security controls.
Close quarters living provides access and
Dissemination of security control workarounds
from observation and readily available expertise.
Social milieu
Peer pressure from others.
Constant presence of co-
Hierarchy of persons in an organisation.
Required to work together with others.
Sense of collective purpose.
Exposed to the actions/behaviours of others.
Peer pressure, often hierarchy based, from more
senior and other residents to disclose private
information, e.g. network credentials.
Constant presence of other residents, even in
private quarters. Security behaviours may be easily
Implied trust due to camaraderie and shared
experiences (based on compulsory attendance of
events) leads to false sense of safety and security.
Information security behavioural threshold
analysis from (Snyman and Kruger, 2019) was
subsequently implemented in the specific context as
described above. For a detail description on
behavioural threshold analysis in general terms, the
reader is referred to (Granovetter, 1978) as only a
brief overview is presented here due to page
restriction considerations.
The threshold questionnaires were digitally
distributed to 186 residents at a single-sex (male)
university residence. Participation was voluntary and
all responses were anonymous. Due to the relatively
sensitive nature of questions that relate to personal
information security behaviour, along with
participation not being compulsory, suitable
responses were obtained from 52 respondents
resulting in a 28% response rate.
The questionnaire consisted of five questions
relating to information security behaviours. To cover
a range of common information security themes,
selected focus areas of the Human Aspects of
Information Security Questionnaire (HAISQ) were
employed as the topics for the questions (Parsons et
al., 2017). The five questions related to
password management, incident reporting, social
media use, internet use, and email use.
A four-point Likert scale was used for the
question responses. The respondents rate their
predisposition for participating in the security
behaviour, relative to the percentage of other group
members that perform the behaviour (Snyman and
Kruger, 2019). This predisposition for participation is
used as the behavioural threshold for the respondent.
Responses from all the respondents were
mathematically aggregated and analysed.
In addition to the questions above that relate to
information security, the questionnaire was
supplemented with questions relating to biographic
information as summarised above. Moreover, the
respondents were asked to rank their own confidence
(five-point Likert scale) in the use of technology (in
broad terms), and more specifically, their confidence
in respect to information security.
Of these 52 respondents, 17 were self-identified
as first-years (typically 19 years old), 15 as second
years (20 years old), 13 as third years (21 years old),
and 7 as being fourth-year and above (22 years old
and over). Additionally, 7 academic faculties were
represented in the responses namely, Faculties of
Education, Engineering, Natural sciences,
Economics, Health sciences, Humanities, and Law.
The distribution of responses per faculty is presented
in Figure 2 below.
Figure 2: Distribution of responses per faculty.
The relatively low response rate and the possible
influence of phenomena such as selection bias
notwithstanding, the distribution between four year-
groups and seven faculties were considered to be
representative enough to allow for the useful
application of information security behavioural
threshold analysis (Snyman and Kruger, 2019). Thus,
no attempt was made to address the possible selection
bias in this specific context but may be investigated
as a possible extension of this study in future work.
In the following section, a reflection is provided
on the aforementioned contextual factors and how
they are echoed in the behavioural threshold analysis
To interpret the behavioural thresholds that were
reported by the respondents, the thresholds are
aggregated by calculating the cumulative frequencies
for each threshold interval. In order to simplify the
analysis, behavioural thresholds are grouped into
intervals of 10%. These frequencies are then graphed
as a line of participation level  versus cumulative
behavioural thresholds  . Furthermore,
Granovetter (1978) stipulates that the cumulative
frequencies of the respondents’ behavioural
thresholds should be graphed in relation to a uniform
distribution of thresholds. This uniform distribution is
referred to as the equilibrium line and is represented
by the  line. The intersection (if present) of the
two lines may indicate that the group behaviour has
reached an equilibrium point, i.e. the number of
participants in the behaviour has stabilised.
Behaviour that has reached equilibrium will not gain
any new participants but neither will any participants
desist from their current behaviour.
Once again, due to the page limit, only one of the
abovementioned security topics (i.e. internet use) can
be shown here. Figure 3 shows the behavioural
threshold analysis graph for internet use for all the
respondents that live in the residence.
Given an initial stimulus like an instigator that
sparks the initial participation in a behaviour, the
number of people that exhibit the behaviour will most
likely grow.
Figure 3: Behavioural threshold analysis graph – Internet
use (All respondents).
From Figure 3, participation in inadvisable
behaviour, relating to internet use, is predicted to
increase to a level where 69% of the inhabitants of the
residence will be performing the unwanted behaviour
if 70% of the group are already performing (or
thought to be performing) the behaviour. It should be
noted that the 70% do not actually have to exhibit the
behaviour. The mere perception that a number of
others are performing the behaviour is enough to
exceed the individual thresholds.
The number of residents that partake in the
behaviour stabilises at this point. This can be deduced
from the intersection of the cumulative threshold line
with the equilibrium line at the point 70, 69.
Granovetter (1978) states that the requirement for
equilibrium is that the two line segments to the left
and right of the intersection have gradients 
∆ ∆
of less than one.
70, 69
0 102030405060708090100
Percentage of respondents who access
dubious websites (Internet use - All respondents)
Behavioural thresholds
Cumulative thresholds
This implies that an equilibrium state requires the
threshold line to intersect the equilibrium line from
above. An intersection from below does not constitute
an equilibrium state, i.e. the gradient is greater than
one. When 1 to the left of the intersection, the
number of participants will not decrease in and of
itself. An external influence or stimulus (e.g.
information security training or awareness
campaigns) is required to reduce the participation
rate. In the same manner, to the right of the
intersection, the number of participants will not
When relating the participation in internet use to
the two external contextual factors that were
mentioned earlier, i.e. physical and social factors, the
influence thereof becomes apparent. The
participation rate of 69% indicates that the
respondents, who all live in the residence, are quite
willing to follow the example of their fellow
residents. Their behavioural thresholds are low, i.e. it
takes little motivation or the perception that only a
few others already perform the behaviour, for them to
also perform the behaviour.
On the physical level, this may be attributed to the
access that the respondents have to technologically
knowledgeable peers. An example scenario can
include that institutions often employ firewalls and
other network tools to prohibit access to websites and
other network protocols they deem to be dubious in
terms of security or questionable in terms of the
content they provide (Miller and Stuart Wells, 2007).
Examples of these types of websites include, among
others, so-called torrent sites which provide unpaid
access to copyrighted materials via peer-to-peer
networks. Illegally downloading these materials are a
frequent occurrence in tertiary institutions (Gan and
Koh, 2006; Lee et al., 2019). Residences provide the
ideal environment where these restrictions may be
circumvented by a knowledgeable person and the
method of access disseminated to others.
The social factor then determines how
dissemination might take place: The required
awareness that such circumventions are possible is
created through constant presence and observation.
The person that originally exploited the
circumvention is then either coerced to help others
bypass the existing security (through peer pressure or
levels of hierarchy) or might provide others with the
solution willingly because of a sense of solidarity and
collective purpose. These factors are therefore
reflected in the willingness of 69% of the respondents
for accessing dubious websites, given that a critical
number of others in the residence already do it.
As mentioned before, the graph in Figure 3 is
representative of the predicted behaviour for the
entire surveyed group. A question that asks
respondents to identify the number of years that they
have been living in the residence was added to the
questionnaire beforehand which allows one to drill
down and identify behaviour for sub-groupings
within the greater group. A finer-grained approach
allows for a more comprehensive analysis. This
allows for pinpointing where different groupings are
persuaded to follow security behaviour differently.
To illustrate this difference, the same internet use
example which was presented for all the respondents
in Figure 3, is now presented for a smaller grouping
in Figure 4, i.e. first-year residents.
Figure 4: Behavioural threshold analysis graph – Internet
use (First-years).
Following the same analysis as described above,
it is interesting to see that the predicted participation
rate for adopting unwanted internet use behaviour for
first-years (20%) is considerably lower than that of
the greater group at 69%.
This implies that the first-years’ thresholds for
participation is higher in comparison with the greater
group. They are therefore less likely to be influenced
in participating in the undesirable security behaviour.
In this research, the self-assigned grouping
classification of first-year is taken to indicate that the
respondent has entered the residence for the first time
20, 20
0 102030405060708090100
Percentage of respondents who access
dubious websites (Internet use - First-years)
Behavioural thresholds
Equilibrium Cumulative thresholds
at the start of the current academic year. This means
that, by the time of distributing the questionnaires to
the residents, first-years would only have been
staying in the residence between one to two months.
It stands to reason that the limited time that they were
functioning in this environment would mean that the
physical and social factors would not have been
experienced as strongly as the other groupings who
have typically been living in the residence for at least
more than a year.
The concept of access to expertise, as a physical
factor, only works if there is a certain rapport that
exists between the parties. A first-year might not (yet)
have the required level of acquaintance or
hierarchical standing (social factor) that affords this
access. Furthermore, first-years do not necessarily
have a sense of camaraderie with the senior students
in the residence. There have not been enough shared
experiences in their frames of reference, but this
shared reference does exist between first-years as
they have undergone the same orientation period
when first joining the residence.
In the final section, the study is summarised. The
aims of the study are revisited, and a reflection is
provided on the contributions and limitations of this
research. A look ahead to possible future work
concludes the article.
In this paper, an investigation was conducted into
contextual factors that might influence information
security behaviour. Section II described contextual
factors that might influence human behaviour.
Section III related these contextual factors to
information security behaviour. Behavioural
threshold analysis, which might consider contextual
factors in information security behaviour, was
presented in Section IV and selected findings of an
application thereof were highlighted.
In Section 1 the original aims of this research were
presented and are therefore reflected upon here.
These aims are reiterated here and are subsequently
discussed. This study aimed to 1) theorise on the
external factors (i.e. external to an individual) that
influence information security behaviour, and 2) to
present the application of a model (behavioural
threshold analysis) that takes context into account and
predicts information security behaviour of a group.
These two aims were addressed as follows:
External contextual factors in information
security behaviour - Five contextual factors in human
behaviour were identified from literature. The
contribution of this research lies therein that these
contextual factors were grouped into two categories,
i.e. intrinsic factors and extrinsic factors. These
categories were then incorporated into a conceptual
framework relating to the Theory of Planned
Behaviour. Guided by this framework, the external
factors were linked with information security
behaviour in general. It was then motivated that the
Threshold Models of Collective Behaviour and
Behavioural Threshold Analysis could be applied to
measure security behaviour, given the influences of
the external contextual factors.
Information security behavioural threshold
analysis – In order to apply the aforementioned
behavioural threshold analysis, a research exercise
was conducted by distributing questionnaires on
group security behaviour at a university residence.
This research contributes by using this specific
contextual environment to explain what form the two
external factors that influence behaviour might take
on in terms of security behaviour within a university
residence. The results of the behavioural threshold
analysis were used to illustrate how the group (and a
sub-group) might eventually follow unwanted
security behaviour. Lastly, the two external
contextual factors were once again discussed with
reference to the outcomes of the exercise and how
these factors might differ between the main group and
the sub-group.
The aims, as reflected upon above, were met
amidst certain limitations which should be noted and
considered when the findings are interpreted: The
study was conducted at one single-sex residence. This
means that there is no corroborating evidence, of the
influence that these specific external factors have on
information security behaviour, from other
residences. Furthermore, only the two external
contextual factors, i.e. physical and social, were
incorporated in the analysis.
These limitations notwithstanding, this research
demonstrates that contextual factors (with specific
reference to extrinsic factors) play an important role
in information security behaviour. These factors may
be analysed by employing models such as
behavioural threshold analysis. Such an analysis may
provide a useful understanding of the human aspect
of information security, and related behaviours, in an
organisation. Better insight into these factors can
contribute to more effective management of the
human factor by guiding information security training
programs to address specific, rather than generic,
security behaviours.
Finally, future studies may consider studying how
the intrinsic factors (even though they are
conceptually part of the TPB) are reflected in the
behavioural threshold analysis model.
The authors would like to thank Mr Johan Allers for
his assistance in distributing the questionnaire.
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