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Thus far implementation of Context Aware Recommender Systems have primarily focused on what to recommend by deriving results from patterns of behavior and environment to determine optimum product selection for recommendation. Our experiment demonstrates that a purchaser’s affective state also has an effect on their perception of information presented via a mobile device. We posit that the ‘how’ and ‘when’ to recommend are important considerations that have not been fully addressed when considering the display of recommendations. Together with user behaviors associated with purchasing traits, e.g. impulse buying, we explore the information processing styles of mental imagery and analytical processing; risk acceptance; involved user effort; and marketing techniques of positive and negative appeals. Results show that these different methods of presenting information to the purchaser will be successful in obtaining a positive user perception within different affective states. Together an understanding of these information presentation and processing techniques is used to build a representation of a purchaser’s perception that could be used in m-commerce systems.
Citation preview
Message Perception within Context-Aware Recommender Systems
Mark A. Hooper, Paul Sant
University of Bedfordshire,
Department of Computer Science and Technology,
University Square, Luton, UK, LU1 3JU
[email protected], [email protected]
ABSTRACT
Thus far implementation of Context Aware
Recommender Systems have primarily focused
on what to recommend by deriving results
from patterns of behavior and environment to
determine optimum product selection for
recommendation. Our experiment
demonstrates that a purchaser’s affective state
also has an effect on their perception of
information presented via a mobile device. We
posit that the ‘how’ and ‘when’ to recommend
are important considerations that have not been
fully addressed when considering the display
of recommendations. Together with user
behaviors associated with purchasing traits,
e.g. impulse buying, we explore the
information processing styles of mental
imagery and analytical processing; risk
acceptance; involved user effort; and
marketing techniques of positive and negative
appeals. Results show that these different
methods of presenting information to the
purchaser will be successful in obtaining a
positive user perception within different
affective states. Together an understanding of
these information presentation and processing
techniques is used to build a representation of
a purchaser’s perception that could be used in
m-commerce systems.
KEYWORDS
Recommender systems, personalization, user
interfaces, affective computing, context-aware
1 INTRODUCTION
Research is beginning establish an under-
standing of user affective, social and phys-
ical states and their relevance within con-
text-aware systems [1]. However it is only
now with the advance of smart-phone sen-
sor technology that research can truly lev-
erage this knowledge within the area of
mobile recommender systems [2]. Though
research into context-aware recommender
systems is now showing positive results
through multi-criteria evaluation of both
user generated content and environmental
context the utilisation of contextual infor-
mation is still thus far limited.
The focus of this paper is to demonstrate
that user context can be used to understand
how an individual reacts to information
presentation styles via a mobile device. We
posit that understanding user behavior
within context is critical to fully realise the
potential for recommender system results
through message customisation, especially
within the developing area of m-commerce
environments. To support this we define
and partly verify a framework for recom-
mender system personalisation that intro-
duces a new layer of system intelligence
through the use of message customisation
based on user contextual behavior.
We discuss the theory that mood and emo-
tions influence our selection of cognitive
processing modes which in turn provide an
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 59
insight into the level of message persua-
sion. To develop our perception trait model
we have developed hypotheses that focus
on the relationships between affective
states, cognitive capacity and behavior. It
is generally agreed that positive moods re-
sult in reduced capacity and therefore a
favouring towards heuristic processing,
whereas negative moods can facilitate
more complex detail analysis [3].
Different affective states can also influence
different purchaser traits including, moti-
vation [4], [5], impulse buying [6], com-
pulsive buying [7], brand attitude and ad-
claim recall [8], risk-taking and self-image
[9]. Myers and Sar [8] provide valuable
insight into how a pre-existing mood af-
fects a user’s response to imagery inducing
advertisements. We show that understand-
ing these cognitive ability and behaviors
should strengthen recommendation con-
version when coupled with standard rec-
ommender techniques.
The rest of this paper is structured as fol-
lows. We investigate a number of affect
behavior relationships and their affect user
perception in section 2. We then discuss
our implementation of an Android applica-
tion used to capture ‘in the wild’ user per-
ception of specific messaging styles, see
section 3. In section 4 we present and ana-
lyse our results and in section 5 we discuss
limitations and opportunities for further
research. Section 6 presents our final con-
clusions.
2 AFFECTIVE PURCHASING
BEHAVIOR
2.1 Consumer Behavior and
Advertisement Techniques
We hypothesise that understanding behav-
ior towards a set of situational contexts can
be utilised to optimise context-aware sys-
tems by providing a reasoned reaction to-
wards, not only the presented options, but
also the method of presentation to the user.
We stipulate that the addition of affective
phenomena to the contextual picture is to
also consider the user’s behavior as reac-
tional and not just as an additional element
of the context that influences preferences.
We can thus potentially indicate behavior
towards the advertisement content and the
medium (i.e. text, image or video), as dis-
cussed in the paper by [10]. This hypothe-
sis leads us to consider behavior as a key
concept to advance research within Con-
text-Aware Recommender Systems (CARS),
thus providing further potential for solu-
tions to commercial recommender system
that operate in complex environments tar-
geting audiences with distinct catalogue
product types numbering in their millions.
Though an everyday occurrence the act of
purchasing an item, whether in store or on-
line, is a complex process that includes
both environmental factors and consumer
characteristics, marketing and environment
stimuli, motivation and personality factors.
There are many drivers that form an indi-
vidual’s approach to the purchasing cycle.
These complex emotional drivers include
social potency and closeness, stress reac-
tion, control, harm avoidance, traditional-
ism, and absorption [6], enjoyment [11],
and perception of risk [12]. These in turn
influence purchasing behaviors of impulse
[6], need for convenience and information
search [13]. Personality traits generally
form our emotional responses to situations
so are key to understanding particular pur-
chasing behaviors such as impulse buying
[6]. As its name implies, impulse buying is
an unplanned event that is made through a
‘snap’ judgment process. By reviewing
stimuli to form a quick, convenient repre-
sentation of a situation it is often character-
ized as a type of holistic processing that
has advantages of speed, and reduced cog-
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 60
nitive effort [14].
A typical example of a holistic processing
technique is mental imagery, this is an in-
fluential tool for advertisers for enhancing
brand attitudes while engaging consumers
[8]. The process not only includes the mar-
keting message cues of visual, auditory,
tactile and emotional [15], but also draws
upon the purchasers previous experience,
memories and daydreams to fully form a
visual image of the situation [13]. This
contrasts with analytical processing which
forms a comprehensive understanding of a
situation through analysis of individual
stimulus characteristics. Burroughs [14],
determines that the style of processing is
selected depending on the characteristics
of task, stimulus and the individual con-
sumer.
An individual’s purchase behavior can be
predicted through their perception of risk,
a consumer will avoid impulse buying
when perception of risk is high [16].
Bhatnagar et al. [12] report on relation-
ships between risk, convenience and on-
line shopping stating that certain product
categories. Music and CD’s, are not gener-
ally considered risky because of the practi-
calities of shopping on-line, i.e. reduction
of costs and an increase in convenience to
make purchases more likely [12]. Products
with higher value are perceived as to have
a higher risk, however they could be
viewed as being more convenient to be
purchased on-line if more involved [12], or
are likely to require an evaluation process
or other pre-purchase activity [13].
Evaluation processes used in information
search rely upon analytical information
processing to produce a comprehensive
understanding [14]. Information search via
the use of mobile phones is important in
the evaluation of alternatives and pre-
purchasing activities, e.g. finding discount
vouchers [13]. Using an analytical pro-
cessing style the individual will attempt to
understand details of the purchasing situa-
tion from all angles, in doing so they will
be more likely to identify all important in-
formation including negative factors and
therefore be able to limit risky conse-
quences [14].
In addition to considering styles of infor-
mation processing, risk acceptance and
levels of processing effort we should also
understand how common techniques for
manipulating emotions are important in
marketing campaigns. We have briefly
mentioned emotional drivers that shape our
decisions and behavior, the use of emo-
tional appeals in marketing create a psy-
chological reaction that could be resolved
by acting upon the appeal message, e.g.
through purchasing an item [17]. Fear ap-
peal has been widely used in commerce
and awareness campaigns with varied suc-
cess depending on content and severity of
message [17], however the basic premise is
to focus on insecurity and concerns in or-
der to prompt action. Positive appeals also
exist and are written to engage arouse
emotions like love, desire or humour to
invoke behaviors including self-esteem
[18]. So we can summarize the above by
identifying four categories that help form
knowledge of consumer engagement with
marketing messages and around which we
can build our hypotheses:
Processing style – mental imagery vs.
analytical
Risk acceptance – low risk vs. high risk
Cognitive capacity – low effort vs. high effort
Appeal type – positive vs. negative appeals
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 61
2.2 The Influence of Emotion upon
Consumer Behavior
This section discusses a number of hypoth-
eses that together will represent a broad
understanding of user perception (and thus
potential user behavior) for use with
CARS. We expect that by determining a
user’s affective state as being positive we
will be able to establish a different set of
likely behaviors when compared to a nega-
tive state. This notion would then support a
systems’ approach in presenting certain
information or taking a specific action.
Myers and Sar [8] discuss the relevance of
mood and its likelihood as a context for an
advertisement to be successful. Alongside
previous research efforts they state that
their findings appear to show that positive
evaluation of an advert is enhanced when
in a positive mood through the increased
ability to undertake mental imagery pro-
cessing. They also suggest that capacity to
evaluate detailed information is reduced
during periods of positive mood but this
then increases during periods of negative
mood. This is supported by Escalas [19],
who notes that the effort in generating the
mental imagery decreases the ability to un-
dertake further cognitive tasks such as crit-
ically analyse the adverts’ content which
could in turn produce more negative evalu-
ations.
These findings suggest that mood is a use-
ful context when ascertaining how to pre-
sent items via a recommender system. The
use of mental imagery may act to make the
recommendation more appealing as mood
positivity increases and thus conducive to
the actual success of the advert. Where a
negative mood is present and mental im-
agery deemed less favourable then recom-
mender messages that provide detail suited
to analytical processing could be more
successful. Presenting recommender items
through the use of mental imagery or ana-
lytical processing depending on the user’s
affective context are a novel concepts,
therefore we posit that:
H1: Processing Style
H1a: that a positive correlation will be
achieved between user affective state and
the perception of a mental imagery induc-
ing statement
H1b: that a negative correlation will be
achieved between user affective state and
the perception of a statement using analyt-
ical, detail-oriented reasoning
The relationship between risk-taking and
mood holds a similar theme. Research has
often reported that when we are in a posi-
tive mood and are presented with a hypo-
thetical situation we are more risk favoura-
ble. For example Yuen and Lee [20], note
that those in a positive mood are less con-
servative and more open to risk. However
they do report significant differences of the
effect of mood on levels of risk ac-
ceptance. This could be explained by not-
ing Isen [21], who suggests that when a
person is presented with a real risk situa-
tion they are more likely to be risk adverse.
Therefore, along with other research such
as [22] we postulate that negative mood is
more complex than basic categories of la-
boratory induced moods of ‘sad’ as used
by [20]. In addition to this it may also be
logical to suggest that real life situational
mood and emotions may potentially pro-
duce different results to laboratory find-
ings, especially under different situational
contexts.
Previous research has also determined the
effect of mood on our perception of risk.
Lee [16], presents results that demonstrate
that elements of positive mood are related
to impulsive buying traits. Brave and Nass
[9] state that it is expected that we will en-
deavour to maintain a positive sensation by
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 62
being more risk adverse with engagement
likely to continue with low-risk impulse
sales. In addition to this, when in a nega-
tive state we are generally aiming to recap-
ture a more positive outlook and are more
likely to engage with riskier purchases to
kick-start the positive emotional process
[9]. We follow this reasoning for the next
hypothesis pair.
H2: Risk Acceptance
H2a: that a positive correlation will be
achieved between user affective state and
the perception of a statement with a low
risk focus
H2b: that a negative correlation will be
achieved between user affective state and
the perception of a statement with a high
risk focus
An interesting consideration for the use of
mobile devices is the presentation of in-
formation in an accessible, intelligent
manner. The size of device and our general
preference for convenience should influ-
ence the way information is presented.
Large sections of text may be off putting to
a user, or indeed be preferred, depending
on their mood or other situational context.
Martin [3] reviewed several research ef-
forts. He summarises that happy moods
lean towards a shallower, heuristic pro-
cessing due to a lowered cognitive capacity
e.g. probably through being distracted or
when in a pleasant environment. Whereas
sad moods suffer more effortful processing
potentially due to a more problematic envi-
ronment. With this research in mind we
explore the following hypothesis.
H3: Cognitive Capacity
H3a: that a positive correlation will be
achieved between user affective state and
the perception of a statement with low ef-
fort processing
H3b: that a negative correlation will be
achieved between user affective state and
the perception of a statement with more
effortful processing
The use of positive and negative messag-
ing in adverts and other action appeals
have been widely discussed and used in
both industry and public sector for dec-
ades. Simple optimistic appeals to traits,
such as self-esteem [18], are commonplace
and provide positive messages to encour-
age actions that will produce a positive
outcome. Fear appeals are a different tech-
nique which are more complex and require
a greater understanding of how negative
thoughts are transferred to the user in order
to promote an action [23]. A prime exam-
ple of a use for fear appeal is the health
awareness warning focussing on long term
change, see review by [24].
Mood has also been shown to have an ef-
fect on both positive and fear appeals. We-
gener [25] observed that someone in a pos-
itive mood would be persuaded more by a
positively framed message than would a
person in a negative mood. They also
found that the opposite occurred for nega-
tively worded messages, with those in a
negative mood being more susceptible to
fear appeals. We adopt Wegener et al [25],
findings to support our next pair of hy-
potheses.
H4: Appeal Type
H4a: that a positive correlation will be
achieved between user affective state and
the perception of an optimistic appeal
statement
H4b: that a negative correlation will be
achieved between user affective state and
the perception of a fear appeal statement
The above hypotheses will enable us to
first investigate previous claims that mood
affects both cognitive capacity and user
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 63
behavior and then develop a generic model
for message style manipulation for rec-
ommender systems and other platforms i.e.
on-line advertising. This will then, in theo-
ry, provide a platform on which to build,
with following research focusing in on
specific purchasing traits which could be
manipulated using bespoke engagement
strategies.
2.3 A Measurement of Perception
To conduct our experiment we require a
simple yet effective measure of user per-
ception. The following section discusses
the rationale for a three point questionnaire
to gauge user perception of specific mes-
sage styles in order to gauge likelihood of
engagement and conversion from browser
to purchaser.
Liu et al. [26] observe that researchers and
practitioners must understand consumer
perception in order to be effective in the
area of mobile advertising. Research into
context has partly enabled this; however,
understanding of mobile user context is
still incomplete and is a popular area of
research. User perception is fundamental to
understanding a user’s attitude towards the
advert (Aad) and thus the likelihood of an
adverts’ success [27]. Aad is the positive or
negative feelings towards an advertise-
ment, service or product within a particular
context and has a strong impact on pur-
chasing [28]. Concepts of perception and
its measurement are complex [29] and with
the measurement of Aad there is also uncer-
tainty. Even early work ascertained that
Aad, as a mediating casual variable that in-
fluences purchase intension, could follow
many possibilities [27]. Research attempt-
ing to measure Aad has produced multiple
measurement scales, Bruner [30] identifies
75 multi-item measures involving 53 dif-
ferent semantic differentials and conserva-
tively suggests an openness within the
field towards measures of Aad. Drawing
upon simple measures used to calculate Aad
to in turn imply perception we opt for a
very short and broad measure of attitude to
gauge opinion. After analyzing the differ-
ent semantic differentials presented by [30]
we reject examples that were deemed to be
more applicable to the specific content that
an advert likely to present e.g. informative-
informative and beautiful-ugly. We select
three very general semantic differentials
that capture a measure of formed attitude
and also encapsulate the majority of those
shown to have been used previously. These
are effective-ineffective, appealing-
unappealing and believable-unbelievable.
Though we are utilizing the term effective-
ness as part of our perception measure its
use should not be compared with the term
advertising effectiveness. Advertising ef-
fectiveness is widely understood to be the
final measure of an advert where the con-
sumer actually makes a purchase. Research
in this area spans decades e.g. [31], [32].
Attitude towards an advert or recommen-
dation is clearly key towards its success
[32], thus our use of effective-ineffective is
to capture a general measure of the sub-
jects’ personal perception of whether a
message is capable of producing a deep
impression or achieving its intended result.
In addition to this our use of believable-
unbelievable is to represent the user’s per-
ception of the message’s credibility, which
Lutz et al., [27] identify as a determinant
of advert attitude. The recent world-wide
study by [33] reports that credibility is a
fundamental component towards advert
effectiveness. This is consistent with the
wider research community’s opinion of the
impact of trust of mobile advertisements
and recommender systems [26].
With regards to differential appealing-
unappealing appeal is obviously a personal
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 64
thing. It is clear that visually appealing ad-
verts accelerate a consumer’s intention to
purchase [34], however other content is
also important. Park et al. [35] determine
that appeal to emotions will be particularly
appropriate to advertisements within the
mobile environment. Hadija et al. [28] note
that users of social media do notice em-
bedded advertisement’s but quickly disre-
gard them to focus on other content such as
friend’s profiles and pictures. It is clear
that this is mostly due to the user’s focus
on the ‘task in hand’ but is also identifies
that an advertisement must focus on char-
acteristics of attractiveness, design and use
of colour to be successfully appealing [28].
Therefore we posit that a purchaser’s per-
ception of an object’s appropriateness and
design, i.e. appeal, is critical to understand-
ing likelihood of engagement and success
in its objectives.
Though simple, our three semantic differ-
entials provide an aggregate of key ele-
ments to retrieve a realistic understanding
of user message perception within real-life
situations using mobile devices. Its use is
discussed in the following section.
3 METHOD
We were keen to not follow other research-
er methodology of stimulating mood states
through techniques such as the use of
mood eliciting video within a laboratory
environment , e.g. [8], and favoured utili-
zation of natural ‘in the wild’ moods and
emotions within a mobile device context.
To complete the experiment needed to cor-
roborate our hypotheses required a robust
smart-phone application (Android) that
was user friendly, able to package data se-
curely whilst also ensuring relative unob-
trusiveness.
The following sections describes the ap-
proach to substantiate the trait behaviors
discussed earlier.
3.1 User Interaction
The main aim of the experiment was to
capture user affective state, i.e. emotions
and/or mood and measure user perception
feedback to specific statements crafted to
prove our hypotheses.
Though there have been some successes in
using sensors and other data to establish a
subject’s mood or emotions see [36], we
determined that a user’s ability to self-
diagnose (emotional intelligence) is still
more reliable and easier to implement via
the mobile phone for this stage of our re-
search. Many measures of emotion and
mood have been explored and theories
abound. However due to their relative sim-
plicity dimensional theories tend to be the
favoured approach where users are asked
to perform a self-diagnosis. We adopt
Mehrabian’s [37] Pleasure-displeasure,
Arousal-nonarousal, Dominance-
submissiveness (PAD) as it is a dominant
dimensional model which has been shown
as an effective method of modelling emo-
tions and other affective states [38]. Our
hypotheses require a scale of positive-
negative affect which directly correlates
with the pleasure-displeasure scale of PAD
[37]. In addition to this the three dimen-
sional approach provides additional granu-
larity against which to further analyse our
results and if possible determine more in-
depth hypothesis.
We poll the user during periods of natural
device use to capture the user’s self-
reporting of their affective state using a
popular psychological tool developed by
[39] called the Self-Assessment Manikin
(SAM). This three factor graphical scale
provides a quickly understood, effective
user interface, which directly transfer to
the three dimensional PAD scales. Note
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 65
that each scale is measured from one up to
five with five being the maximum value.
Fig. 1 is a screenshot of our Android im-
plementation showing the three SAM
scales with a larger image that pops up up-
on selection of an individual image for
clearer visibility on small screens.
3.2 Determining Likelihood of
Engagement
For testing our four trait hypotheses we
developed 38 statements split over the
types of high and low risk, fear and opti-
mistic appeals, mental imagery and detail
processing, high and low effort. The long-
est statement was 343 characters long
(with spaces) with the average being 136
characters long. We wanted to ensure that
any emotion educing bias was reduced by
not using video, images or any other colour
variation throughout our tests. Therefore
all statements are displayed using the same
simple interface with light blue text on
black background using 18 px Arial. A se-
lection of five statements are randomly se-
lected and displayed, the user is then
prompted to rate each statement as de-
scribed below.
Where previous research including [40],
[41], [42], have provided comprehensive
checklists that capture user attitude to-
wards advertisements we deem that for ‘in
the wild’ testing these are too detailed to
implement. In addition, as we were not as-
sessing adverts per se but rather tailored
statements that singularly focus on a par-
ticular trait we deemed attributes such as
brand reinforcement, empathy, familiarity,
entertainment, in formativeness or state-
ment such as ‘I don’t like it’ unsuitable for
our needs. We therefore opt for a broad
three point questionnaire that focuses on
key elements of perception that have been
shown to be important factors in the suc-
cess of an advertisement, see section 2.3.
We use three semantic differentials of ef-
fective-ineffective, appealing-unappealing
and believable-unbelievable in our meas-
ure of perception. Throughout the rest of
this paper we will refer to this measure as
the eab-perception. Each message state-
ment is subject to the eab-perception, each
differential is rated using 5 point psycho-
metric Likert scale e.g. from 1) Very inef-
fective to 5) Very effective. The three eab-
perception semantic differentials have been
selected as they provide a reflective sum-
mary of the user’s perception of the state-
ment and therefore a likelihood of en-
gagement. This method enables us to pro-
duce a potential negative or positive re-
sponse to a statement. As per typical use of
multi-item we simply use the result of
SUM(eab-perception), Cronbach α = 0.79.
Each statement type is relatively simple in
structure with the focus being on a basic
message to the user for interpretation. The
structure for each type of statement used is
described as follows:
Fig.1 Screenshot of our Android implementation
showing the three SAM scales for capturing the
user’s affective state. For clarity on smaller
screens the larger overlaying image is a ‘pop-up’
which actions on selection of a smaller image
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 66
Mental imagery – statement that only
induces mental imagery i.e. analytical pro-
cessing not required
Detail processing – statement that provides information that needs to be ana-
lysed or compared
Low effort – easy to read statement with shorter word length (18 word aver-
age)
High effort – statement with longer
word length (34 word average) using more
complex statements
Low risk – statement that presents no risk and is very general
High risk – statement that presents a situation with an element of risk
Fear appeal – statement that infers that
inaction will lead to a negative result
Optimistic appeal – statement that in-fers that an action will lead to a positive
result
The reader should note that some of the
statements are structured in a way that they
fit several categories. For example a small
number of high risk and high effort fitted
the definition of a detail processing state-
ment. The reverse however is not always
the case.
4 RESULTS
Our research into the effect of mood and
our perception of different ways of pre-
senting information via a mobile device
draws upon a number of previous hypothe-
ses. Correlation analysis is used to prove
the extent to which ‘in the wild’ affective
state determines the likelihood of each
processing behavior trait being favoured.
Our experiment collected 57 responses
with 58% of responses completed by male
participants. A total of sixteen users partic-
ipated in the experiment, 75% male and
25% female. Though a wide ethnic popula-
tion was included 62% were white British.
The spread of age groups were as follows,
21 years and under (25%), greater than 22
years and less than 35 years (50%), greater
than 35 years (25%). Though we seek
strong correlation for our results we obvi-
ously do not expect perfect values of 1 and
-1 for respective positive and negative cor-
relations for a number of reasons. We
acknowledge the main issues behind the
self-reporting technique used in our data
collection. Firstly the users’ input is sub-
jective, relying upon the user’s emotional
intelligence (ability to self-assess their
mood and emotions). Secondly that both
the input for the PAD assessment and the
perception feedback both utilize the Likert
style scales which can be prone to central
tendency bias.
As our hypotheses are biased towards par-
ticular positive or negative correlations we
test for one-tail correlation the results of
which are represented as r. The probabili-
ties of these are measured using p-values
and where statistically significant are
shown as p<0.05 (confidence level of
95%) or p<0.01 (confidence level of 99%).
Our results are as follows. We find a posi-
tive correlation between level of affect and
the perception of mental imagery state-
ments (r=0.45, p<0.01) thus H1a is prov-
en. However it is not clear that the oppo-
site case applies i.e. a negative correlation
between affect and the perception of ana-
lytical detail processing. Our results show
a minor, insignificant correlation, thus H1b
is not proven. See Fig. 2 for representative
correlations.
The failure of H1b could be explained by
suggesting that though detail processing is
favoured when in a negative state it may
not be true that we are unable to undertake
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 67
analytical processing when in a positive
state. Though the undertaking of mental
imagery may ‘take up’ cognitive capacity
and thus reduce detail processing it does
not necessarily mean that there is an inabil-
ity to conduct some detailed analytical
processing in the absence of mental image-
ry processing. In other words though we
may not completely focus upon the mes-
sage details we will still be able to process
the overall meaning and develop a general
perception of the message [43], and there-
fore produce different results depending on
the situation.
Fig. 2 Correlations for statements using Mental
Imagery and Detail Processing
For H2a we find a positive correlation be-
tween level of affect and the perception of
low risk statements, therefore H2a is prov-
en (r=0.3, p<0.05). However the results
present no significant correlation between
affect and the perception of high risk
statements, thus H2b is not proven. To
help explain this lack of correlation we
note that Lewis et al. [44] present further
levels of complexity into the understanding
of negative emotions. He suggests that var-
ied effects on risk taking can be found with
different types of negative emotions. Con-
flicting effects are not only present when
comparing consequential with reflective
mechanisms but also that anger and fear,
while both negative, have opposing effects
with angry people favouring risk and fear-
ful people being more risk adverse.
Though this argument may provide some
insight we can also see that other context
will have an effect on mobile device user’s
perception of risk. It has been shown that
environmental factors, including sound
[45], [46] and location [47], have a con-
trolling effect on our ability to process in-
formation. In addition to this user activity
such as multi-tasking can also have a det-
rimental effect on comprehension [48].
With both environment and activity having
an impact on user ability to process infor-
mation we therefore suggest that a user
could fail to form a satisfactory perception
of risk under certain circumstances, espe-
cially if the message requires careful delib-
eration.
Our hypotheses on perceptions of effort
follow a similar pattern to H1 and H2. A
relatively low positive correlation (r=0.28,
p<0.05) has been found between user af-
fective state and the perception of a state-
ment with low effort processing. However
no useful correlation is present between
user affective state and the perception of a
statement with more effortful processing.
Therefore H3a is proven and H3b is not.
As previously mentioned [8], research has
suggested that those in a negative emotion-
al state would be more favourable towards
processing effort. However an explanation
for this suggests that this could be due to a
problematic situation [3]. Knowledge of a
user’s situation could be key to under-
standing the lack of correlation in hypothe-
sis H3b. Not only is situational context
very likely to affect cognitive ability and
willingness to engage, the limitations of
the mobile device is also likely to be a fac-
tor in preventing a user from fully engag-
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 68
ing in effortful information processing
[49].
The pair of hypotheses involving both op-
timistic and fear appeals are also only part-
ly validated. The expected positive correla-
tion between user affective state and the
perception of an optimistic appeal state-
ment was successfully achieved (r=0.45,
p<0.01). The results for the perception of
fear appeal statements which as expected
produced a negative correlation was reject-
ed with no significance found. See Fig. 3
for representative correlations.
Fig. 3 Correlations for statements using Positive
Appeal and Fear Appeal
The value of these findings is important in
that if a recommender system is armed
with values for affective state then it can
determine how best to present the recom-
mended item. For example irrespective of
the product being recommended when the
user is in a positive state the system could
use mental imagery to maximize purchase
conversion rates and when negative use
different techniques such as increasing
brand awareness or product comparisons.
Understanding how a user’s mood shapes
their response to risk also potentially ena-
bles a system to determine how and when
to present certain higher risk items, for ex-
ample an expensive holiday. The same un-
derstanding can also be applied for levels
of effort and likelihood of engagement.
5 DISCUSSION
Whilst our ‘in the wild’ experiment has
confirmed that correlations are present be-
tween some behavior traits and levels of
affect the results show that not all cases are
proven. We reject hypotheses that require
complex analytical effort, fear appeal or
acceptance of higher risk. Concerns would
be raised if converse correlations were
achieved however we see zero (or very
close to) correlations. The spread of re-
sults, in particular for negative mood, sug-
gests that either some emotions are more
complex than tested for or that other fac-
tors are influencing the relationship be-
tween mood and the behavior traits. This
highlights the main limitation within our
experiment and shows that a larger set of
results is need for analysis against addi-
tional user contexts. In addition to this
more realistic ‘adverts’ or recommender
explanations and items important to the
user are needed to further prove the hy-
potheses developed.
While not all hypotheses have been proven
we can at this stage still present an insight
for recommender message personalization
to users in both positive and negative
moods even without considering other con-
texts. The basic behavior trait model utiliz-
es logic for personalization for users in
positive or negative affective states, see
Fig. 4. The caveat of consider_using
shown in the logic allows the option to uti-
lize different engagement techniques when
the user is not in a positive mood. While
our results from H1, H2, H3 and H4 do not
prove that techniques such as detail pro-
cessing will always be effective when the
user is in a negative state they do show that
the opposite technique, in this case mental
imagery, will not be effective. Therefore,
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 69
selecting the consider_using options poten-
tially provides a higher chance of success.
Obviously a system that is achieving in-
sight into a purchaser’s affective state by
means other than self-diagnosis will also
have access to other contextual infor-
mation. Therefore a system will be unlike-
ly to solely utilize basic message personal-
ization described in Fig. 4 and will also
draw upon additional factors that inform
understanding of purchaser perception and
likelihood of engagement. The user’s envi-
ronment, their levels of activity and the
company of others will all have an effect
on ability to conduct actions of processing
information (whether consciously or not).
If one or more of the inputting context be-
come extreme then the purchaser may be
unable to filter out their effect causing the
perception likelihood to become less pre-
dictable. Under these situations the system
may choose to only engage in the simplest
manner, say to just increase brand aware-
ness, or indeed decline to engage com-
pletely.
Fig. 4 Pseudo code representing logic for selecting
basic message personalization.
So, to be truly successful in their integra-
tion into m-commerce recommender sys-
tems the personalized, perception-aware
interface must incorporate a range of clear-
ly understood contexts. Through personal-
ized presentation of recommender messag-
es the system utilizes the most appropriate
format and therefore influences behavior.
The focus of our next stage of research is
to further our understanding of this con-
cept. This will enable a revision of the log-
ic within our basic trait model with the uti-
lization of additional context introducing a
greater certainty when considering options
for engagement.
6 CONCLUSION
In this paper we have addressed the fol-
lowing. 1) Whether an understanding of
user behavior traits and user affect can be
used to determine how best to present in-
formation in order to increase the likeli-
hood of m-commerce engagement. 2)
Whether context-aware recommender sys-
tems can adopt behavior trait models
alongside specific user context to deter-
mine how and when to present recommen-
dations.
For our first research question we conclude
that if the affective state of the user is
known then behavior traits can be used to
inform how best to present information to
a mobile user. We have shown that mes-
sages presented ‘in the wild’ via a smart-
phone which employ methods of mental
imagery, low effort, low risk and positive
appeals produce increasing levels of posi-
tive perception as user mood improves.
Though our results suggest that the same
does not apply to detailed analytical pro-
cessing, higher effort, fear appeals and
higher risk methods they do still provide
support for a basic behavior trait model.
We conclude that context-aware recom-
mender systems can adopt behavior trait
models to determine how to present a rec-
ommended item. We have also indicated
that with a greater understanding of the
impact of other user contexts then detailed
IF affective_state = positive
THEN using {
mental imagery
positive appeal
low risk
low effort }
ELSE
THEN consider_using {
detail processing
fear appeal
higher risk
higher effort }
Proceedings of the Third International Conference on E-Technologies and Business on the Web, Paris, France 2015
ISBN: 978-1-941968-08-6 ©2015 SDIWC 70
analytical processing, higher effort, fear
appeals and higher risk methods could also
be used within message customisation and
the presentation of recommender items.
Therefore our closing conclusion is that a
context-aware recommender system armed
with a comprehensive behavior trait model
will be able to determine the how and when
to present recommendations for optimized
engagement and thus improve return on
advertisement investment in m-commerce
contexts.
Future work will be directed towards strat-
egies of engagement and message person-
alization within recommender systems.
Our focus will be within context-
awareness, and the understanding of user
behavior within contrasting situations.
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ISBN: 978-1-941968-08-6 ©2015 SDIWC 73