The Impact Of Product Presentation On Decision-Making And Purchasing

Abstract

Purpose

This paper investigated how apparel product presentation influences consumer decision-making and whether there are any differences between age groups.

Design/methodology/approach

A mixed methodology was employed, including eye-tracking and qualitative in-depth interviews, with a purposive sample of 50 participants aged 20-70.

Findings

A higher number of product presentation features resulted in increased positive visual, cognitive and affective responses as consumers wanted as much visual information as possible to aid decision-making. Images of models attracted the most attention and were the most influential product presentation feature, followed by mannequin images and the zoom function. The 20s spent much less time viewing and interacting with the product presentation features than middle age groups (30s-50s), had minimal fixations on mannequin images and had a much quicker decision-making process than other age groups.

Practical implications

The research informs retailers which product presentation features are the most effective for their target market in order to aid consumer decision-making with the aim of reducing returns.

Originality/value

The paper contributes to the literature by providing more in-depth insights than previous studies into the impact of online product presentation on consumer decision-making by using qualitative research and eye-tracking. The research also explores more product presentation features than previous research and investigates the presentation of apparel products, which are notoriously the most difficult products for consumers to assess online. The research is unique in its exploration of age differences in relation to product presentation features.

Full text

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Citation: Boardman, R., McCormick, H., (2019). ‘The Impact of Product Presentation on
Decision Making and Purchasing’, Qualitative Market Research: An International
Journal, Vol. 22 No. 3, pp. 365-
380: https://www.emeraldinsight.com/doi/pdfplus/10.1108/QMR-09-2017-0124
The Impact Of Product Presentation On Decision-Making And Purchasing
Abstract
Purpose
This paper investigated how apparel product presentation influences consumer decision-
making and whether there are any differences between age groups.
Design/Methodology/Approach
A mixed methodology was employed, including eye-tracking and qualitative in-depth
interviews, with a purposive sample of 50 participants aged 20-70.
Findings
A higher number of product presentation features resulted in increased positive visual,
cognitive and affective responses as consumers wanted as much visual information as
possible to aid decision-making. Images of models attracted the most attention and were the
most influential product presentation feature, followed by mannequin images and the zoom
function. The 20s spent much less time viewing and interacting with the product presentation
features than middle age groups (30s-50s), had minimal fixations on mannequin images and
had a much quicker decision-making process than other age groups.
Practical Implications
The research informs retailers which product presentation features are the most effective for
their target market in order to aid consumer decision-making with the aim of reducing
returns.
Originality/Value
The paper contributes to the literature by providing more in-depth insights than previous
studies into the impact of online product presentation on consumer decision-making by using

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qualitative research and eye-tracking. The research also explores more product presentation
features than previous research and investigates the presentation of apparel products, which
are notoriously the most difficult products for consumers to assess online. The research is
unique in its exploration of age differences in relation to product presentation features.
Key words: Eye-Tracking, Product Viewing, Product Presentation, Product Image,
Website Design, Age

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Introduction
Online sales account for 24% of the total spent on fashion in the UK and it is estimated that
sales will reach £16.2billion in 2017 (Mintel, 2017). However, there is a perceived element of
risk involved for consumers when shopping online as they cannot physically touch or try on a
garment (Jai, O’Boyle and Fang, 2014). This means that the way that garments are presented
online is extremely important for helping consumers in their decision-making process. An
eye-tracking study conducted by Wang, Cui, Huang and Dai (2016) found that rich product
presentation online captures the most attention. However, Wang et al. (2016) acknowledged
that their findings were limited by the products that they asked consumers to view (a laptop/
electronic dictionary) as they were search products, and therefore called for future research to
investigate the presentation of experience products, where the product typically needs to be
seen and tried on before purchase in order to successfully evaluate it (Gupta and Harris,
2010). Garments can be considered experience products and the difficulty of evaluating how
well they fit, without trying them on, often results in consumers abandoning their transaction
or buying multiple sizes and returning products they dislike (Drapers, 2017). The present
study addresses the call by Wang et al. (2016) by looking at the presentation of apparel
products on retailers’ websites in order to gain further insights into how it influences
consumers’ decision-making. In online retailing the inability to physically examine an item is
magnified for apparel products as they require sensory inspection to assure adequate fit and,
thus, the product presentation is the most important aspect of online retailing for apparel
(Young, Kwon and Lennon, 2007). No previous study has investigated if there are any
differences in responses to product presentation between age groups and so the present study
also fills this gap by investigating consumers over a fifty-year age span. Using the Stimulus-
Organism-Response (SOR) model, the product presentation features tested were:
1. Product Images: i). Mannequin ii). Model iii). Back iv). Fabric
2. Zoom-function
3. Product videos
Hence, this paper contributes to extant literature by investigating two research questions:
RQ1: How do product presentation features influence consumers’ decision-making on
apparel websites?
RQ2: Do different ages of consumers have different responses to different types of product
presentation features on apparel websites?

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The paper contributes to the literature by providing more in-depth insights than previous
studies into the impact of online product presentation on consumer decision-making by using
a combination of qualitative research and eye-tracking. The research also explores more
product presentation features than previous research by examining all the features on a
typical retailer’s website today and investigates the presentation of apparel products, which
are notoriously the most difficult products for consumers to assess online, hence the huge
issue of online returns prevalent in the industry – 42% of womenswear garments bought
online are returned (Drapers, 2017). Finally the research is unique in its exploration of how
product presentation influences different ages of consumers’ decision-making, which is of
particular importance due to the ageing population and older people now shopping online.
Theoretical Framework: Stimulus-Organism-Response Framework (S-O-R)
The research draws its theoretical foundations from environmental psychology and is
organised according to the Stimulus-Organism-Response (S-O-R) paradigm (Mehrabian and
Russell, 1974). This paradigm advocates that the environment affects the emotional state of
the individual, which, in turn, has an impact on their behaviour. This study extends extant
research by examining how online product presentation affects consumers’ internal states
(visual, cognitive and affective), which in turn affects their shopping outcomes
(approach/avoidance behaviour) with age as a moderator (Eroglu, Machleit and Davis, 2001).
The S-O-R framework has been used in many studies to gain an understanding of how online
environmental cues affect consumers’ responses (see Jai et al., 2014; Kim and Lennon,
2010). Thus, it is a reliable model to use in this research context. The S-O-R framework
created for the present study is shown in figure 1.
Picture

Zoom

Video

Visual
Perception

Affective

Cognitive

Approach/
Avoidance
Behaviour
Age
Stimulus Organism Response

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Figure 1. Research Model
Stimuli: Product Presentation
With the majority of purchases made within the physical store (Mintel, 2017), it is clear that
apparel websites are not providing consumers with effective enough product presentation in
order to help them in their decision-making. The fit, colour and quality of garments are
difficult to evaluate online (Park, Lennon and Stoel, 2005) but retailers are attempting to
overcome this challenge by providing a variety of different types of product presentation
features to enhance consumers’ decision-making when shopping for apparel online. Such
product presentation features include, product zoom, 3D images and catwalk videos (Park et
al., 2005; Wang and Benbasat, 2009). Studies have shown that the provision of accurate and
detailed visual information of products decreases consumers’ perceived risk of shopping
online and helps them in their decision-making (Park et al., 2005) and different visual
product presentations affect different areas of the brain during consumers’ decision-making
processes (Jai et al., 2014). Yet no previous study has examined which product presentations
features are more effective than others in aiding consumers in their decision-making, and
whether a consumer’s age is a factor in this.
i. Product Images
Research has found that apparel retailers can minimise consumers’ perceived risk of
shopping online by using human models to display garments, so that they can see how the
clothes fit (Kim and Lennon, 2010). Indeed, images of human models increase the likelihood
of producing an emotional response from on an online consumer, are likely to get more
attention and be more appealing to users (Cyr and Head, 2013; Wang, Yang, Wang and Ma,
2014). Therefore, extant research suggests that online retailers should present the clothes on a
human model in order to increase its attractiveness (Wang et al., 2014).
ii. Zoom-function
The zoom-function is important as it enables consumers to see garment details before making
a purchase, indeed, Siddiqui, O’Malley, McColl and Birtwistle (2003) found that consumers
wanted a detailed zoom-function along with the ability to view the product in 3D form.
Hence, for online clothes shopping, image zoom evokes an increased visual perception of the
product when consumers are evaluating it (Jai et al., 2014) and so including a zoom-function
would improve the website (Karimov, Brengman and Van Hove, 2011).

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iii. Product videos
Algharabat, Alalwan, Rana and Dwivedi (2017) found that 3D product presentation quality
had an impact on users’ attitudes towards the product, the website and on their level of
satisfaction. Research has found that product videos are more effective than images in having
an impact on the consumer experience and can increase consumer trust (Karimov et al.,
2011). Indeed, Park et al. (2005) found that product movement increased consumers’ sense
of pleasure, decreased their perceived risk and increased their purchase intention.
Furthermore, Jai et al. (2014) found that rotation videos may make consumers believe a
product will be a higher price. However, the product videos tested in many previous studies were
simple rotation ones (e.g. Park et al., 2005), and so catwalk videos need to be tested to see if the
findings are still applicable, which the present research will do. Thus, our research will investigate
how different types of product presentation features influence consumers’ decision-making
on apparel websites.
Organism: Affective and Cognitive Processes And Visual Perceptions In Online
Decision-Making
The consumer decision-making process involves the identification of a problem, the search
for information, the development of an alternative, a purchase, and the evaluation of the
overall outcome (Chae and Lee, 2013). The process is very analytical and rational, but it
cannot be understood simply through the analysis of the final decision (Chae and Lee, 2013).
Therefore, research must analyse the perceptual, emotional and cognitive processing that
results in a decision, in order to obtain a true understanding of the consumer decision-making
process (Chae and Lee, 2013). Hence, in order to gain a greater understanding of the
consumer decision-making process in response to product presentation, an understanding
must firstly be gained of consumers’ intervening organisms.
In the S-O-R model the organism represents the intervening internal processing that is
undertaken by the consumer between the stimulus and the response (Loureiro and Roschk,
2014). It is these processes that enable consumers to transform stimuli into meaningful
information to understand the online environment before making a decision (Koo and Ju,
2010). The organism comprises of consumers’ perception, physiological responses, feelings
and cognitions that result in a change in their cognitive and emotional state (Koo and Ju,

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2010). In the present study, the organism is represented by the consumer’s visual perception,
affective response and cognitive perception of the stimuli.
Visual perception is part of a consumer’s response when they evaluate apparel online and is
the internal processing of visual information seen in the environment onto the retina and then
to the visual cortex (Jai et al., 2014). The present study uses eye-movements in the form of
fixations to represent visual perception. As the primary sensory experience when shopping on
a retailer’s website is visual (Kim, Lennon and Stoel, 2010), visual perception is an important
concept that requires further investigation (Jai et al., 2014). The cognitive and emotional
processes must also be examined to gain a better understanding of consumers’ decision-
making processes (Chae and Lee, 2013). The cognitive states are everything that the
consumer thinks when acquiring, processing, retaining and retrieving information (Eroglu et
al., 2001). Affective reactions are the subjective feelings that a consumer experiences in
response to the stimuli (Oh, 2005). Previous literature indicates that eye fixations are linked
to the cognitive part of the organism, as it represents cognitive processing (Wang et al. 2014),
or the affective part of the organism, as it is an involuntary emotional response (Alghowinem,
AlShehri, Goecke and Wagner, 2014). In the present study, in-depth interviews will be used
to interpret the consumer’s cognitive and affective responses to the stimuli to support the eye-
tracking data.
Response: Purchase Intention and Approach/Avoidance Behaviour
The response is the approach or avoidance behaviours of a consumer as a result of the
organism process (Eroglu et al., 2001). Approach behaviours are all the positive behavioural
actions that are achieved, such as an intention ‘to stay, explore, and affiliate’ and avoidance
behaviours are simply the opposite (Eroglu et al., 2001), both of which can be related to eye-
movements (Guo, Cao, Ding, Liu, and Zhang, 2014). The present study will investigate
whether particular types of product presentation have an impact on consumers’ approach or
avoidance behaviour.
Moderator: Age
As the UK population is ageing, there is an increasing number of less mobile older
consumers shopping online for convenience- 78% of UK internet consumers aged 65+ now
shop online (Telegraph, 2016) and there has been an increase in women aged 35-44 shopping
online in the last year (Mintel, 2017). The ageing population indicates the need for

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researchers to better understand and investigate the technical and psychological requirements
of older consumers (Cyr, 2014). Loureiro and Roschk (2014) found that graphics resulted in
more positive behavioural intentions from younger participants than it did from older
participants concluding that younger consumers require more stimulation. Research has also
found that older people find assessing product information more difficult and they often have
to repeat steps in the purchasing process (Kuo, Chen, and Hsu 2012). Therefore, not all age
groups behave the same online and so it is important to take this into account in research.
Yet, despite the increasing rise of mature consumers using the internet and the indication of
behavioural differences between age groups from previous research, there is a limited amount
of academic research on this topic. Young consumers are researched more than older
consumers because managers place a greater emphasis on their customers under-50 and
because of the perception of older people not being interested in, or having the skills to shop
online (Loureiro and Roschk, 2014). However, due to the ageing population and older people
increasingly using technology, websites must be designed with both older and younger
consumers in mind (Romano Bergstrom, Olmsted-Hawala and Jans, 2013). The present study
will fill this gap by investigating how age could generate differing cognitive, affective and
visual behaviour in response to different product presentations on apparel websites, providing
valuable insights for academics and practitioners.
Method
This study employed a mixed methodology consisting of eye-tracking and qualitative in-
depth interviews. Eye-tracking provides insights into what catches consumers’ attention on
websites, facilitating a detailed understanding of their perceptions of the website (Djamasbi,
Siegel and Tullis, 2010). This study used the video-based Tobii TX 300 eye-tracker which
has 300 Hz sampling rate. It uses infrared sensors underneath the monitor to capture the
corneal reflection, which makes the pupil very bright and easy to detect for the eye-tracking
analysis software. It is unobtrusive, operating like a desktop computer without additional
requirements such as a chin rest (Tobii). The compensation for large head movements
enables participants to move freely and naturally, whilst still ensuring accurate
measurements, improving the validity of the experiment (Tobii). The Tobii TX 300 can
capture data accurately regardless of participants’ age, ethnicity, glasses, contact lenses,
mascara or ‘droopy’ eyelids, making the data highly reliable (Tobii).

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Consumers’ responses to the product presentation were analysed using a live website in real
time. This is unique, as the majority of eye-tracking research is based on static web pages
(e.g. Djamasbi et al., 2010), and is therefore limited in its generalisability. Using a live
website enabled users to interact with the product presentation features as they would do
during their normal online shopping journey and their eye fixations and cognitive processing
could be captured and analysed instantly in real time, making the results more realistic and
therefore more valid. Participants conducted a free-browsing task, whereby they were
instructed to browse the website to find dresses that they were interested in and look at them
in further detail and add them to their bag if they would purchase it, replicating their typical
online shopping experience. Participants were asked to specifically look at dresses to narrow
the category of clothing down to create a level of standardisation in the results and avoid too
much variation when analysing their response to the product presentation features as different
garment shapes/types might encounter different viewing behaviour. Thus, the data analysed
participants’ responses when they interacted with all the product presentation features
(stimulus) on the product information page (of a dress) on a live website. There was no time
limit placed on the task in order to try and make it as realistic as possible (Pieters, 2008), and
thus participants could look at as many products as they wanted. The time spent on the site
did not have an impact on the results as it was the participants’ responses to the individual
product presentation features that were analysed, and no participants looked at fewer than 5
products. The average time spent on the site was 8-9 minutes, with the shortest time being 4.5
minutes spent and the longest 13 minutes.
The eye-tracking data was analysed through gaze-replay videos and heat maps based on
consumers’ fixation durations. A fixation was defined as 250 milliseconds in line with
previous literature (Guo et al., 2014). Gaze-replay videos are screen recordings of the eye-
tracking experiment containing the participant’s gaze behaviour overlaid on the video and are
an effective way of analysing users’ visual behaviour on dynamic content (Wong, Bartels,
and Chrobot, 2014). This enabled the researchers to effectively analyse how users interacted
with the product presentation. Heat maps are an effective and compelling way to
communicate complex findings as they are very clear and easy to understand, showing the
distribution and concentration of attention through different colours (Wong et al., 2014).
They are colour-coded in accordance with the number, or the degree, of fixations each area
attracts: red areas show the stimuli that received the most attention, the green areas are the
ones that received the least, and the non-coloured areas received no attention (Wang et al.,

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2014). Thus, the present study used heat maps to show the fixation durations of participants
in order to demonstrate their visual perception of the product presentation.
A qualitative in-depth interview was conducted after the eye-tracking in order to gain an
understanding of peoples’ experiences with the product presentation. All interviews followed
the same framework and question guidelines so that they could be compared easily. The
questions were structured as follows: main questions, probes and follow-up questions (Rubin
and Rubin, 2012). An interview guide containing the questions can be found in Appendix 1.
An analysis technique of coding was employed in order to generate the meanings from the
data and an understanding of participants’ cognitive and affective responses to the different
product presentations and their approach/avoidance behaviour and purchase intention. The
codes and main themes of the interviews were generated inductively.
A purposive sample of 50 female participants were chosen according to the following
criteria: they were regular customers of the website, had purchased at least one dress from it
within the previous 3 months and they were aged 20-69. 10 participants in each age group
were recruited which is large enough to draw valuable conclusions in eye-tracking studies
when comparing age groups (Bol, Romano Bergstrom, Smets, Loos, Strohl, van Weert,
2014). This provides a more realistic understanding of online shopping behaviour by loyal
consumers, which has not previously been addressed in research. To increase reliability, a
pilot study involving 6 participants was conducted prior to the research to assess any
problems.
Results and Discussion
The results revealed that product presentation significantly influenced consumer decision-
making, which was analysed as the way that users searched for products (they clicked on the
images of the dresses they liked), the way they assessed the product (what they looked at on
the product information page) and whether they then added it to their bag or not (indicating
purchase intention), and the evaluation of the overall outcome (captured through the
interviews) (Chae and Lee, 2013). The eye-tracking results showed that participants’ age had
an effect on their visual perception of product presentation features and the in-depth
interviews showed that participants’ age also facilitated different cognitive and affective
responses to product presentation features. These differences influenced consumer

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approach/avoidance behaviour. The eye-tracking and in-depth interview results are analysed
and discussed according to the research questions below.
RQ1: How do product presentation features influence consumers’ decision-making on
apparel websites?
Participants wanted to see a large variety of images: ‘…The more images you’ve got… gives
you more confidence in what the item will be like…’ (P.49). Therefore, a higher number of
product presentation features, such as the model and mannequin images, the back view, a
close-up of the fabric, a zoom-function and a product video, resulted in approach behaviour
because consumers’ felt more confident about what they were ordering. Indeed, if the product
presentation did not contain a video or a variety of images of the garment, then this resulted
in avoidance behaviour because consumers could not obtain enough information.
Heat maps showed that participants had longer fixation durations on the model than the
mannequin image, spending significantly more seconds viewing it:
Figure 2. Heat maps of all participants on the mannequin image (left) and the model image
(right)
Thus, participants are more visually stimulated by the model images, supporting research by
Cyr and Head (2013) and Wang et al. (2014) that consumers have a positive emotional
response to model images and pay more attention to them. This was confirmed by the
interviews:‘…on a model it is much better.... I remember buying a… dress and it looked
shaped when it showed you just the photograph, but when it came it was absolutely straight…

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on a model, that would have come across’ (P.33). Having the garment displayed on a model
was more helpful for consumers’ decision-making as they could assess the garment shape and
fit, which concurs with previous research that images of models can minimise consumers’
perceived risk of shopping online (Kim and Lennon, 2010).
However, participants had a negative cognitive response to the size of the models: ‘…it looks
good but you instantly think of yourself and it doesn’t look right for someone my shape…
because you’re not going to look like that… you get a bit upset…’ (P.7). Participants felt that
the models were not realistic in relation to the average size of the consumer and wanted to see
different size and shaped models: ‘…what looks good on one shape doesn’t necessarily look
good on another… show what would suit a certain shape…’ (P.26). Indeed, the interviews
found that having different size and shaped models would positively influence decision-
making: ‘…I know they’re trying to sell a dress, but sometimes I’d like to see just an ordinary
woman in them… these models are all tall and slim… What about short and dumpy people!’
(P.9), an aspect that future research could explore further. This highlights the complexity of
online product presentation for apparel and that generalisations cannot be made from findings
focused on search products (e.g. Wang et al., 2016).
Gaze-replay videos showed that the majority of participants looked at the image of the back
of the garment, and interviews confirmed that participants wanted to see it: ‘…the back
because some dresses you don’t know whether… they’ve got a zip back or a low cut back… if
it’s a V back, how low the V actually goes… to have information… would cause me less
hassle having to return things’ (P.46). Thus, participants wanted to see the back of the
garment in order to gain more information about the product to help them with their decision-
making process. Nevertheless, although participants looked at the back of garment, they spent
less time looking at it than the other product images. This indicates that it is necessary to
include the back image but other functions are viewed more extensively to get a better idea of
material and fit for the decision-making process.
Gaze-replay videos showed that participants often clicked on the close-up of the fabric and
interviews found that participants had a positive cognitive response to it: ‘…I’d like a close-
up of it… it’s helpful… you can tell miles better the pattern and the colors’ (P.42). However,
the analysis of the gaze replay videos showed that the fabric image was not viewed as much
as the zoom-function, which has a similar purpose in providing a more detailed look at the

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material, indicating that the fabric image is less important than the zoom-function for online
product presentation of apparel.
Gaze-replay videos showed that half of the participants used the zoom-function when
assessing the product. Furthermore, interviews found that participants had a positive
cognitive and affective response to the zoom: ‘…I use it a lot… you can get a proper look at
the fabric… that takes away the thing about not being able to see it properly like in a shop’
(P.39). Thus, participants had a positive response to the zoom-function as it enabled them to
obtain an enhanced look at the details, patterns and fabric of the garment. These findings
agree that consumers want a detailed zoom-function and that the zoom increased the visual
perception of the product (Siddiqui et al., 2003; Jai et al., 2014). However, the zoom-function
was also criticized by participants: ‘...even if you zoom right in… it’s still small and you don’t
see as much of it… it should be a full screen image… the more you can see the better…’
(P.6). Thus, the zoom was not considered effective enough for gathering product information,
which may indicate why only half of participants used it. Hence, it is crucial for retailers to
invest in a good quality zoom-function that enables consumers to see the fabric composition
in detail, especially for apparel products, in order to aid them in their decision-making.
Finally, participants had a positive cognitive and affective perception of product videos: ‘…to
see what it looked like from the back and front as they walked would help me make my mind
up’ (P.41). This shows that consumers play videos to gain product and style information,
particularly regarding the movement and the fit of the garment, aiding their decision-making.
The interviews also indicated that product videos influenced purchase intention: ‘They are
really good… to see when you’re walking how it flows… It makes me more inclined to
purchase it’ (P.48). Nevertheless, gaze-replay videos showed that the product videos were the
least viewed product presentation feature out of all the ones investigated. This completely
contradicted the positive response to videos generated from the interviews, but when
participants were probed further, it showed that it was due to a lack of awareness of product
videos being a feature on the website. Indeed some participants were surprised when showed
that they could play product videos, as it was something they did on other retailers’ websites
but not the one used in the study. This shows the importance of the placement of product
presentation features on retailers’ websites, a topic that future research could explore further.

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Hence, a variety of product presentation influenced consumer decision-making with
consumers having a more positive response to a higher number of product presentation
features: ‘…if I can see something really clearly and have a look at it from different angles
then I’m more likely to buy it…’ (P.39). This concurs with Park et al. (2005) and Jai et al.
(2014) who found that having more visual product information produces more positive
emotions and decreases product evaluation difficulty, which can positively influence
purchase intention. However, no previous research has investigated which product
presentation features are the most important in terms of their influence on consumer decision-
making. The present study fills this gap by finding that images of models wearing the
garment, closely followed by mannequin images were the most influential forms of product
presentation on consumer decision-making. The zoom-function was the next most influential
feature, followed by the close-up of the fabric image, highlighting the importance of showing
the detail of the fabric composition for apparel online in order to aid consumer decision-
making. Finally the image of the back of garment and the product videos were found to be
the least influential product presentation features on consumer decision-making out of the
ones tested. However, this may be because the product videos were not clearly displayed and
so further research could test whether placing them in different areas on the product
information page would make them more effective.
RQ2: Do different ages of consumers have different responses to different types of product
presentation features on apparel websites?
The research found that there were differences between age groups in their visual perception
of the product presentation, as shown in Figure 3:
20s 30s 40s 50s 60s
Figure 3. Heat Maps Of Different Age Groups On The Product Image

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The heat maps show that the 20s had the quickest fixation durations on the product image,
whereas the 50s spent the most time looking at it, followed by the 30s. Thus, consumers in
their 20s made a very quick decision and did not study the images for long, whereas the 30s,
40s and 50s took much longer in their decision-making process. The gaze-replay videos
showed that the middle age groups were more likely to use the zoom-function than the
youngest and oldest age groups, as only 20% of the 20s and 60s used the zoom-function
when viewing items, whereas 80% of the 30s and 50s and 50% of the 40s did. This indicates
that the middle age groups used all the available product presentation features and wanted as
much visual information about the garment as possible to aid their purchasing decision. This
highlights that websites designed for the 30s-50s age groups must ensure that there is a wide
variety of product presentation features in order to help them with their decision-making and
encourage purchase intention. This is both an important academic and managerial
contribution as no previous research has considered that age has an impact on the way that
consumers respond to online product presentation, and shows that retailers’ targeting
different age groups should consider this when investing in their product presentation
features.
The reason that the 20s do not spend long looking at or interacting with the product
presentation features and make their decision very quickly may be because they are very fast
when using websites, having grown up as digital natives. This indicates that the 20s are the
most confident online shoppers and content to order products to try on at home without
spending time assessing the details as they are secure in the returns process, which is a
concern for retailers as returns are costly (Mintel, 2017). Hence, retailers targeting younger
consumers should ensure that they have quick-to-use, clear and effective product presentation
features in order to encourage consumers to use them and make more informed decisions
when shopping for apparel online.
In particular, the 20s spent very little time looking at the mannequin image, often not
choosing to look at it at all: ‘I’m not bothered about that… I prefer looking at how you can
wear it and what it looks like on rather than just it on its own’ (P.35). This contrasts
considerably to the older age groups, as represented by the 20s and 60s heat maps:

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Figure 4. 20s mannequin heat map (left) and 60s mannequin heat map (right)
However, there were minimal differences between ages in the amount of attention paid to the
model images, as shown by the 20s and 60s heat maps:
Figure 5. 20s heat map on the model (left) and the 60s heat map on the model (right)
Hence, both older and younger consumers spent more time looking at the model images than
the mannequin images and there were minimal differences between different ages of
consumers in their fixation durations on model images. Indeed, the interviews confirmed that
the 60s age group preferred to look at the model images over the mannequin images: ‘I prefer
the dresses on a model rather than on a mannequin… Because you can see how they hang

17
better, what they look like’ (P.15). This emphasises the importance of including model
images for retailers’ targeting all ages of consumers.
Conclusion
The research found that having a wide selection and variety of images aided decision-making
because consumers’ felt more confident about what they were ordering, indicating that this
could also encourage purchase intention. In particular, images of the garment on a model,
mannequin images and zoom-function all received a considerable amount of attention and
had the most influence on consumer decision-making. The research also found that there
were differences between age groups in their responses to product presentation features.
Consumers in their 30s-50s took longer looking at the product presentation and spent more
time in their decision-making than consumers in their 20s and 60s. In particular, the 20s did
not tend to look at images of mannequins. However, there are minimal differences between
ages in their fixations on model images; they were universally popular. These are novel
findings, showing that age is an important factor to consider in website design studies,
particularly with the ageing population and increasing number of older people shopping
online. The paper also contributes to the literature by providing more in-depth insights into
the impact of online product presentation on consumer decision-making by using a
combination of qualitative research and eye-tracking. This emphasises the importance of
using qualitative research to support eye-tracking research in order provide more in-depth
insights, and hence highlights the methodological contribution of this study. The research
also explores more product presentation features than previous studies and investigates the
presentation of apparel products, which are the most difficult products for consumers to
assess online.
This research has important practical implications for online fashion retailers as it emphasises
the most important product presentation features to include. Fashion retailers should include
model images and mannequin images first and foremost as they are the most influential in
terms of consumer decision-making, followed by an effective and detailed zoom-function and
close-up of the fabric. An effective zoom-function is especially important for retailers
targeting consumers aged 40-59. Retailers targeting consumers in their 20s need to ensure
that they make their product presentation features very clear, detailed and quick-to-use so that
it will encourage them to interact with the product more and make more detailed assessments
of its suitability in order to reduce returns. The findings could potentially help reduce the

18
number of online returns for fashion retailers by highlighting the most important product
presentation features in aiding consumer decision-making and, in turn, encouraging purchase
intention. Further research could be conducted to test this more empirically. The findings
were presented to the online fashion retailer who’s website was used as basis for this study
and they changed the design of their product presentation features as a result, in the hope of
aiding consumer decision-making and reducing returns.
Limitations and Future Research
The limitation of this study is that it consisted of an all-female sample based in the UK.
Therefore, the findings may be different for male consumers and consumers in other
countries and so future research could replicate the study using an all-male sample or a
website from a different country. The research used a sample of regular users of the website
so future research could investigate whether there are any differences between regular and
first-time users of a website. Future studies could also test the appeal of some of the findings,
such as different shaped models and displaying the product videos in a different format.
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Appendix
Main Questions
Probes
Follow-up Questions
Purpose: Necessary to ask in
order to address the research
topic
Purpose: Encouraged
participants to provide
further details and examples
in their answers
Purpose: Got participants to
expand on key themes and
concepts addressed to gain a
greater understanding of the
issue
General Product
Presentation
What do you normally do
when you click on a product
to look at?
Why?
That is interesting; can you
tell me a bit more about that.
What images do you like to
see of the garment?
Why do you want that
selection of images?
How would this affect your
purchase intention?
How would this affect your
decision-making?
Can you talk me through
your typical decision-making
process?
Can you give me an example
of the last time you decided
to buy a product on this
website?
What product presentation
features influenced your
decision making?
What product presentation
Why? How did they
influence you?

22
features influenced your
purchase intention?
Mannequin/ Model Images
What do you think about the
model/mannequin image?
Why do you think that?
That is interesting; can you
tell me a bit more about that?
What would you normally do
when you see the model
image?
Why would you do that?
That is interesting; can you
tell me a bit more about that?
Can you show me your
typical process on this page?
How would the image of the
mannequin/model affect your
decision-making?
Why is that?
That is interesting; can you
tell me a bit more about that?
How would the
model/mannequin image
affect your purchasing
intention?
Why is that?
That is interesting; can you
tell me a bit more about that?
Zoom Function
In your typical shopper
journey, would you normally
use the zoom function or not?
Why/why not?
That is interesting; can you
tell me a bit more about that?
What do you think about the
zoom function?
Why would you use it in that
way?
That is interesting; can you
tell me a bit more about that?
How would the
model/mannequin image
affect your decision-making?
Why would you do that?
That is interesting; can you
tell me a bit more about that?
Can you show me your
typical process on this page?
How would the zoom
function affect your
purchasing decision?
Why?
That is interesting; can you
tell me a bit more about that?

23
Product Videos
In your typical shopper
journey, would you normally
play the product video or
not?
Why/why not?
That is interesting; can you
tell me a bit more about that?
What do you think about the
product video?
Why is that?
That is interesting; can you
tell me a bit more about that?
How would the product
video affect your decision-
making?
Why?
That is interesting; can you
tell me a bit more about that?
How would the product
video affect your purchasing
decision?
Why?
That is interesting; can you
tell me a bit more about that?
Table 1. Interview Guide; Examples of Main questions, probes and follow up questions based
on Rubin and Rubin (2012)