The effects of retail lighting on atmosphere perception
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Custers, P. J. M., Kort, de, Y. A. W., IJsselsteijn, W. A., & Kruiff, de, M. (2009). The effects of retail lighting on
atmosphere perception. In Y. A. W. Kort, de (Ed.), Proceedings of the International Conference on the Effects of
Light on Wellbeing (Experiencing Light 2009), 26-27 October 2009, Eindhoven, The Netherlands (pp. 14-21).
Eindhoven University of Technology.
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Proceedings
EXPERIENCING LIGHT 2009
International Conference on the Effects of Light on Wellbeing
Y. A. W. de Kort, W. A. IJsselsteijn, I. M. L. C. Vogels,
M. P. J. Aarts, A. D. Tenner, & K. C. H. J. Smolders (Eds.)
Keynotes and selected full papers
Eindhoven University of Technology,
Eindhoven, the Netherlands, 26-27 October 2009
Volume Editors
Yvonne de Kort, PhD
Wijnand IJsselsteijn, PhD
Karin Smolders, MSc
Eindhoven University of Technology
IE&IS, Human-Technology Interaction
PO Box 513, 5600 MB Eindhoven, The Netherlands
E-mail: {y.a.w.d.kort, w.a.ijsselsteijn, k.c.h.j.smolders}@tue.nl
Ingrid Vogels, PhD
Visual Experiences Group
Philips Research
High Tech Campus 34, WB 3.029
5656 AE Eindhoven, The Netherlands
E-mail: ingrid.m.vogels@philips.com
Mariëlle Aarts, MSc
Eindhoven University of Technology
Department of Architecture Building and Planning
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5600 MB Eindhoven, The Netherlands
E-mail: M.P.J.Aarts@tue.nl
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The Effects of Lighting on Atmosphere Perception in
Retail Environments
Pieter Custers
Philips Lighting - GOAL
Mathildelaan 1
5600 JM Eindhoven, The Netherlands
+31 40 27 55654
pieter.custers@philips.com
Yvonne de Kort
Human-Technology Interaction
Eindhoven University of Technology
5600 MB Eindhoven, the Netherlands
+31 40 247 5754
y.a.w.d.kort@tue.nl
Wijnand IJsselsteijn
Human-Technology Interaction
Eindhoven University of Technology
5600 MB Eindhoven, the Netherlands
+31 40 247 4455
w.a.ijsselsteijn@tue.nl
Marike de Kruiff
Creative Director Philips Design
Emmasingel 24, Bldg HWD, P.O. Box 218
5600 MD Eindhoven, The Netherlands
+31 40 27 96291
marike.de.kruiff@philips.com
ABSTRACT
The present study's objective was to investigate the
contribution of lighting in evoking an atmosphere in
naturalistic environments, among the extensive set of other
environmental cues. In a field study involving 57 clothing
stores, lighting attributes (e.g., brightness, contrast, glare
and sparkle) and context (i.e. the shop interior) were
assessed and quantified independently. These data were
then used to predict four dimensions of perceived
atmosphere of these stores in multiple regression analyses.
A hierarchical procedure was chosen, with context
variables entered in the first block and lighting attributes in
the second block. We were thus able to determine the
effects of lighting on perceived atmosphere, while
controlling for context effects. Both lighting attributes and
interior qualities were successfully related to perceived
atmosphere. Our most important finding was that, even
given the substantial contribution of design elements in
retail environments, lighting does play a significant role in
evoking atmospheres.
Keywords
Lighting,
environmental
assessment,
atmosphere
perception, retail environments, Multiple regression, cardsorting
INTRODUCTION
As any light designer, light researcher, and even layperson
will confirm, lighting and ambiance are intimately related.
Literature indicates that lighting characteristics can
influence emotions, mood and cognition, and atmosphere
and spatial impressions, although at times the collected
14
findings are inconclusive. With respect to emotions for
instance, some studies report more pleasant emotions with
higher light intensity levels [1], whereas others report no
significant effects [2,3]. Fleisher et al. [1] demonstrated
that a combination of high illuminance levels and a
relatively large indirect lighting component resulted in
higher feelings of dominance. Cool white light was shown
to be arousing [1], while a more complex pattern emerged
in a second study, reporting positive effects of colour
temperature on male participants’ mood, yet negative
effects on females’ moods [2].
Literature reports of several studies investigating the way
people assess lighting directly. Hawkes, Loe and Rowlands
[4] suggest that people categorize lighting using the
lighting characteristics brightness and interest (or
uniformity). Flynn and colleagues [5] added a third
dimension (overhead – peripheral). Unfortunately, both
studies [4,5] used a sample size too small for a robust factor
analysis. Veitch and Newham [6], who tackled this problem
working with 292 participants, demonstrated that people
categorize lighting in terms of the three dimensions:
brightness, visual attraction, and complexity.
Literature also describes how lighting can affect people’s
environmental impressions (for a review see [7]). As one of
the first, Flynn, Hendrick, Spencer and Martyniuk [5] used
a realistic interior (i.e. conference room) and found an
effect of lighting on subjective evaluations of the
environment, perceptual clarity and spaciousness. This
research, together with several follow-up studies,
summarized in [7], suggests that in the North American
society and culture, there are at least six broad categories of
human impression that can be influenced or modified by
lighting design: perceptual clarity, spaciousness, relaxation
and tension, public versus private space, pleasantness, and
spatial complexity (sometimes liveliness). After relating the
impression dimensions to lighting characteristics, Flynn [7]
suggested several design guidelines: For perceptual clarity,
the designer should apply bright and peripheral lighting. An
impression of spaciousness (i.e., the space is perceived as
large) is achieved when applying uniform and peripheral
lighting. Pleasant and relaxing impressions are the result of
peripheral and non-uniform lighting. And lastly, to
establish a ‘private’ impression, the designer can select
non-uniform and dimmed lighting.
Houser, Tiller, Bernecker and Mistrick [8] varied the
direct/indirect lighting ratio and concluded that walls and
ceiling contribute to the perception of overall brightness
when work plane illuminance is held constant. Also, rooms
appear more spacious with higher ratios of indirect lighting,
and rooms with relatively high levels of indirect lighting
are favoured over light settings with less than 60% indirect
lighting. Literature thus establishes that lighting is able to
influence environmental impressions.
Yet although literature reports of studies indicating that
lighting characteristics influence moods and emotions,
cognition, and environmental impressions, there are hardly
any studies that have established these effects outside the
laboratory. Although it is one thing to prove that variations
in lighting in an otherwise controlled environment have an
impact on environmental impressions, showing that
lighting actually contributes to atmosphere perception in
naturalistic environments, i.e., in the real world is quite
another, let alone ascribing this to specific lighting
attributes. This is exactly what the current study set out to
do. And it did so in a type of environment with substantial
variations in interior design, and where atmosphere has
been proven to matter significantly: retail environments.
Retail Environments
Retail environments communicate the stores’ image and
purpose to customers [9], they can evoke emotional
reactions [10], impact the customers’ ultimate satisfaction
with the service [11], and even the money and time spent in
the store [12]. Therefore, creating the right environmental
setting is of prime importance for shop owners. To create
the desired ambiance, lighting may have its contribution,
but it is only one of the numerous elements, such as
furnishing and finishing of the shop’s interior, size,
crowdedness, and music, that play a role.
Different categorizations for these environmental
characteristics are proposed. Bitner [9] suggested three
groups: ambient conditions; spatial layout and
functionality; and signs, symbols and artefacts. Berman and
Evans [13] included the exterior of the shops and came to
four groups: general interior; the layout and design; the
point-of-purchase and decoration; and the exterior of the
shop. Turley and Milliman [14], in turn, added a fifth
category: human variables. Most recently Baker,
Parasuraman, Grewal & Vos [15] proposed a model in
which the environmental cues were divided into three
categories: design, ambient, and social variables.
Since environments include such an extensive variety of
stimuli, while on the other hand consumers perceive
environments holistically [16] it is essential to seek general
variables as descriptors that grasp the main influence of the
environment [17]. Kaplan [18] suggested that four
environmental dimensions can predict preference for an
outdoor environment: complexity, mystery, coherence and
legibility. Environmental complexity refers to visual
richness, ornamentation, information rate, diversity and
variety in an environment [19], and is shown to have a
linear relationship with interest (arousal) and a curvilinear
(inverted U) relationship with preference (pleasure)
[19,20,21], meaning that moderate levels of complexity are
most preferred. Another important environmental
dimension is order [20], which is related to the extent of
coherence, legibility, organization, and clarity of an
environment [19]. In studies of urban environments
(summarized by Nasar [22]) order has been shown to have
a positive impact on pleasantness and a negative impact on
arousal. Except for the inverted U relationship between
complexity and pleasantness, all these relationships are
confirmed for retail environments [23].
We conclude that lighting has a potential contribution to
perceived ambiance, but is only one of the numerous
elements that may play a role. Our question was whether
lighting would play a role that was measurable, and if yes,
which lighting attributes would have the most substantial
contribution.
METHOD
Design
Fifty-seven clothing stores participated in a field study,
exploring the contribution of lighting to environmental
impressions, controlling for other contextual influences.
For each of these stores the three categories of variables –
perceived atmosphere, lighting attributes, and context (i.e.,
the shop’s interior design) – were assessed and quantified.
Assessments were made independently of each other, by
different groups of experts (lighting) or lay people
(atmosphere, context). We then performed multiple
regression analyses on perceived atmosphere dimensions
with lighting attributes and context as independent
variables.
Participants & Shops
For this field study 57 shops were selected. The stores were
all located in the city centre of Eindhoven, a mid-size
Dutch city, to enable participants and experts to visit all the
shops in one morning or afternoon. In order to prevent
statistical confounds caused by the type of product sold,
15
only fashion shops were selected to participate1. Low and
high-end shops were avoided for the same reason. Within
this selection of shops, which still presented a wide variety
of shop interiors and fittings we expected that structural
confounds between lighting configuration and interior
design would be limited. Nonetheless, in order to control
for this eventuality we also assessed and quantified the
style of the shops’ interiors.
To assess context, i.e., the interior design of the stores,
twenty participants were recruited from a participant
database of the university. The group consisted of ten males
and ten females, ranging in age between 19 and 44, with an
average of 28 years. The respondents were not familiar
with the shops participating in the study.
Seven lighting experts participated in the assessment of the
lighting and lighting fixtures in the stores. Their ages
ranged between 29 and 58, with an average of 46, five were
male and two female.
For quantifying perceived atmosphere, six participants
were recruited from the university’s database. The
participants did not have specific affinity to lighting or the
shops participating in this study. Three participants were
male and three were female. Their ages ranged between 22
and 29, with an average of 24.5 years.
Measurements & Procedure
Context Characterization
A card-sorting experiment was performed to characterize
the shops’ interior designs. Pictures of these interiors were
printed on A5 photo paper and served as cards. The
photographs were all taken inside the shop, from the same
position at which participants rating the atmosphere (see
below) would be standing. In taking the pictures, we
avoided photographing ceilings and lighting fixtures where
possible. Initially two pictures were taken per shop. After a
pilot study we reduced the number of cards to 87, by
removing one picture per shop if both pictures were always
categorised in the same groups. The participants performed
the experiment individually to assure independence of
grouping strategies [24].
totally applicable), based on the chosen quality. This was
repeated, until the participant could not come up with
another discriminating quality.
In total the 20 participants performed 59 categorizations.
Multiple correspondence analysis was then performed on
these data, yielding two dimensions on which the shops
varied (inter-dimensional correlation -.006). We labelled
them ‘legibility’ (order-disorder) and ‘warmth’ (warmcold), based on the labels participants had given for their
categorizations. Each shop’s scores on these dimensions
were used in the multiple regression analyses reported
below, to account for the variability of shop interiors.
Lighting Attributes
A panel of experts assessed the lighting in the shops during
a site visit. For this they used a questionnaire developed
also in cooperation with lighting experts. The questionnaire
consisted of 31 items, probing established lighting
attributes such as brightness, contrast (i.e., uniformity),
colour temperature, glare and sparkle, and modelling, as
well as the relative contribution of different types of
lighting (i.e. general, accent, architectural, decorative) and
the lighting installation (see Table 1). Each of the seven
experts filled out one questionnaire per shop (i.e., 7 times
57 in total) individually. They visited the shops between ten
o’clock in the morning and half past noon, avoiding the
busiest hours. Also, their visits were scheduled within a
period of three weeks, to minimize the chance of interiors
being redecorated. Order effects, e.g. as a result of learning,
tiredness or boredom, were controlled by varying the order
in which each expert visited the stores.
Inter-rater reliabilities were computed to determine the
level of agreement among the experts. Cronbach’s alpha’s
between experts’ scores for each individual item ranged
from .635 to .940, with an average of .804 (see Table 1).
These reliabilities were more than satisfactory, indicating a
high level of agreement among the experts in scoring the
lighting attributes of the shops. The scores of the experts
were averaged to compute each shop’s score.
Participants were instructed to think of a discriminating
quality they felt could serve as a base for sorting the shops,
e.g. ‘cluttered’. They then sorted the pictures of the shops
into five piles2 (ranging from totally not applicable to
1
Since the type of lighting often differs with the type of
product, yet product class may also influence atmosphere
perception, this could result in structural relations
between lighting and ambiance not really attributable to
the lighting per se.
2
Although a division over five piles was desired, the
participants were instructed to first create three piles – not
applicable, neutral or applicable. Then they were asked to
divide the neutral pile into three piles again – less
applicable, neutral or more applicable. This resulted in 5
piles in total. This procedure was followed because the
16
pilot study pointed out that this procedure would lead to
the most evenly spread division of the pictures over the
five piles.
Table 1. Inter-rater reliabilities of lighting questionnaire items
Atmosphere Perception
Item
In the third phase, six (new) participants also visited all the
shops (following different routes, to vary the order in which
shops were assessed) and rated the ambiance in each of
them. For measuring perceived atmosphere a short version
of Vogels’ [25] instrument was used. This questionnaire
measures perceived atmosphere in four dimensions:
cosiness, liveliness, tenseness and detachment. After
deliberation with Vogels, 18 of the original 38 items were
selected (4 or 5 per dimension), with seven-point Likert
scales ranging from totally not applicable to totally
applicable. Participants scored each shop on each of these
items. They were not aware that the study was focused on
lighting and were not instructed to pay particular attention
to lighting or lighting fixtures.
Cronbach’s
alpha
Item
Cronbach’s
alpha
General lighting
.940
Accent lighting
.942
Decorative lighting
.805
Architectural lighting
.933
Brightness
back walls
.870
Brightness
horizontal plane
.823
Brightness ceiling
.820
Brightness floor
.819
Brightness
side walls
.892
Brightness overall
.915
Colour temperature
light
.759
Colour temperature
total space
.813
Glare
.889
Sparkle
.822
Luminance ratio
back walls
.789
Luminance ratio
horizontal plane
.825
Luminance changes
back walls
.691
Luminance changes
horizontal plane
.719
Luminance ratio
ceiling
.635
Luminance ratio
floor
.765
Luminance changes
ceiling
.677
Luminance changes
floor
.638
Luminance ratio
side walls
.816
Luminance ratio
overall
.766
Luminance changes
side walls
.775
Luminance changes
overall
.773
Conspicuous
lighting installation
.628
Patterned
lighting installation
.778
Amount of fittings
.906
Different fittings
.841
Modeling
.865
Mean
.804
Internal consistencies of these atmosphere dimensions were
determined by calculating Cronbach’s alpha for each of the
six participants (see Table 3). Averaged values indicated
acceptable (>.60) to good (>.80) reliabilities. The level of
agreement between participants was determined by
calculating inter-rater reliabilities (Cronbach’s alpha) per
dimension. The values are reported in Table 3. Correlations
between the scores on the different atmosphere factors are
displayed in Table 4.
Table 3. Internal consistencies and inter-rater reliabilities of the
atmosphere scales
Factor analyses (Principal Component with Varimax
rotation) of the data resulted in six dimensions qualifying
attributes of the lighting configuration: contrast, brightness,
glare and sparkle, contrast on the ceiling, aesthetics of
lighting installation, and decorative lighting. The score for
each of the dimensions was determined by averaging the
scores of the items contributing to that particular
dimension. For instance the score for the factor glare was
calculated by averaging the scores for accent lighting, glare
and sparkle. Correlations between the six factors are
reported in Table 2.
Table 2. Lighting attributes correlation matrix
bright
ness
contrast
bright
ness
glare &
sparkle
contrast
of ceiling
lighting
install.
.402
glare &
sparkle
contrast
of ceiling
lighting
install.
decor.
Lighting
.620
-.056
-.092
.089
.399
.165
.206
-.198
-.051
.041
.047
.202
-.111
.043
Each shop’s scores on these lighting attributes were used in
the multiple regression analyses reported below, to account
for the variability of the shop lighting.
Average internal
consistency*
Inter-rater
reliability**
Cosiness
.83
.65
Liveliness
.77
.76
Tenseness
.79
.42
Detachment
.61
.84
*: averaged over 6 participants’ individual internal consistency
scores; **: between the 6 participants’ scores on that dimension.
Table 4. Correlations between scores on atmosphere dimensions
Liveliness
Tenseness
Detachment
Cosiness
.330
-.613
-.309
Liveliness
1.000
-.340
-.789
1.000
.310
Tenseness
RESULTS
Multiple regression analyses were performed predicting
perceived atmosphere dimensions with the two context
variables and the six lighting attributes as predictors. Note
that in these analyses, the 57 shops were the cases (they
made up the rows in the statistical database). Four separate
analyses were performed - one for each atmosphere
dimension.
We first performed multiple regression analyses on
atmosphere dimensions, exploring only lighting attributes
as candidate predictors in a stepwise procedure. The
17
obtained significant beta-weights are displayed in Table 5.
Brightness contributed significantly to three atmosphere
dimensions: cosiness (negatively), tenseness and
detachment. Contrast significantly decreased perceived
tenseness. Glare & sparkle contributed significantly to
liveliness and negatively to detachment.
Table 5. Significant beta coefficients of regression analyses
without context variables
Lighting
characteristics
R"
Brightness
Not controlled for context effects
Cosy
Lively
Tense
Detached
.336**
.312**
.180
.249*
-.588***
.484**
Contrast
.354*
-.362*
Glare & Sparkle
.469**
-.382*
Note: Results of 4 separate regression analyses, with the 4 atmosphere
dimensions as respective dependent variables. N=57. * p<.05, ** p<.01,
*** p<.001
! coefficients
Step 1
Warm
R" change
Legibility
.246
Warm
.384**
Block 2
(lighting)
.279 **
.058
.119
.051
-.212
-.116
.189
Block 2 (lighting)
.445 *
Glare & sparkle
.043
Contrast of the
ceiling
-.059
-.499 **
Glare & Sparkle
-.007
Contrast of
ceiling
-.206
.039
Lighting
installation
-.157
Lighting
installation
-.153
Decorative
lighting
.102
Decorative
lighting
Note: * p<.05, ** p<.01, *** p<.001
Note: * p<.05, ** p<.01, *** p<.001
Table 6D. Hierarchical regression predicting detachment
Table 6B. Hierarchical regression predicting liveliness
! coefficients
Step 1
R"
Detachment
R" change
Step 2
Legibility
-.590 ***
-.496 ***
Warm
-.247 *
-.146
Contrast
.093
Brightness
-.128
Step 1
.115
.806 ***
.765 ***
Warm
.056
.033
Brightness
R" change
.652 ***
Legibility
Contrast
R"
Step 2
.682 ***
Block 2 (lighting)
.522 ***
Block 2 (lighting)
! coefficients
Block 1 (context)
.407 ***
Block 1 (context)
.013
.170
Glare & sparkle
-.175
Glare & sparkle
.293 *
-.123
Contrast of the
ceiling
-.003
Contrast of the
ceiling
.158
Lighting
installation
-.064
Lighting
installation
-.026
Decorative
lighting
.033
Decorative
lighting
18
.130
-.298
Brightness
Brightness
Liveliness
R" change
.059
Contrast
Contrast
R"
Step 2
Block 1 (context)
-.132
.281 *
! coefficients
Step 1
.105
-.158
Tenseness
Step 2
Block 1 (context)
Legibility
R"
We then repeated the analyses, yet this time controlling for
contextual variables. A hierarchical procedure was chosen,
with context descriptors comprising the first block and
lighting attributes the second block. We could thus
determine the effects of lighting on perceived atmosphere
while controlling for context effects. In the first block,
context variables were entered (Table 6). Adding the
lighting attributes after this first block generally improved
the predicted variance. Moreover, for three atmosphere
dimensions, at least one lighting attribute had a significant
beta-weight. Brightness significantly and substantially
decreased perceived cosiness, and increased perceived
tenseness. Glare and sparkle contributed to the perceived
liveliness of fashion stores. Furthermore, the shops’
legibility was shown to significantly decrease perceived
liveliness and increase perceived detachment.
Table 6C. Hierarchical regression predicting tenseness
Table 6A. Hierarchical regression predicting cosiness
Cosiness
Controlled Regression Analyses
.030
DISCUSSION
Light and ambiance are intimately related, yet we know of
very few studies that have attempted to measure how much
lighting actually contributes to atmosphere perception in
naturalistic environments. The current study attempted to
do just that. Also, we hoped to attribute any contribution
we might find to more or less specific lighting attributes.
And indeed we did manage to verify that lighting
contributes a measurable part to atmosphere assessments.
This contribution was modest, and we did not establish
significant effects for each dimension of atmosphere, but in
view of the challenges we met, our findings were certainly
satisfactory.
Measuring light’s contribution in naturalistic settings
proved to be quite a complex exercise. For one, one is
dependent on the natural range and variance of lighting
used in ‘real’ settings, and has to find a way of categorising
or even quantifying that. In the current study, experts
scored the lighting in each of the 57 shops, using a
questionnaire specially developed to this end. Inter-rater
reliabilities between these experts indicated that this
produced a reliable and robust measure, which was more
detailed and comprehensive than what could have
realistically been possible with objective measurements.
A second obstacle in natural settings is accounting for the
substantial variance and contribution of intervening
variables. Based on the literature, we expected that
especially the shop’s interior and social variables would
play an important role in defining the atmosphere. The
social setting we tried to control by selecting time slots that
were not too long and avoided the busiest hours. The
shops’ interiors were controlled first by limiting them to a
certain type of product (clothing) and excluding the
extreme ends of the price levels. Second, since this still left
us with a huge range of different interiors – e.g. cluttered to
spacious, old-fashioned to trendy, warm wooden furniture
to cool metal racks and stands – we made an attempt to
characterise and quantify these interior styles using the
card-sorting method. These data enabled us to characterise
all 57 shops by their location in a two-dimensional space
stretching from orderly to disorderly and from warm to
cold. We were not able to control the soundscapes (e.g., the
music playing in the shop) or the shops’ exteriors.
A third obstacle in the present research was measuring
ambiance or atmosphere. We were not aware of existing
standardized instruments for measuring atmosphere in retail
environments, or other types of environments for that
matter. Instruments most often used are probably the sets of
semantic differentials, similar to the one we used in the
present study. We preferred this measure [25] to other ones,
for instance the well-known set developed by Russell,
Mehrabian, and colleagues (e.g., see [17]), since it was
specifically targeted to atmosphere perception, and its
dimensions appeared closer to what we intended to measure
than the dimensions typically coming from those sets
(generally something like evaluation, arousal and potency).
The current instrument worked well in terms of the internal
consistencies of its subscales, yet in hindsight it does not
necessarily cover all relevant aspects of atmosphere. Also,
it could have been interesting to also have probed
characteristics such as ‘spaciousness’ or ‘perceptual clarity’
directly. This would have made it easier to compare the
present study’s findings to those reviewed earlier, for
instance by Flynn [7]. However, we felt the current
measure was closer to the ‘atmosphere’ concept, and we
had to restrict the number of items, since each participant
would have to fill out the questionnaire 57 times (!), one for
every shop.
However, we feel that with these 57 shops, we have
managed to create a large enough sample to guarantee a
good variance in our core dependent and independent
variables: lighting attributes and atmospheres, and to
perform the multiple regression analyses on. We were in
fact quite happy and proud to have been able to recruit that
many shops to participate in the study. This potentially also
illustrates the interest of these shops’ owners in the role that
lighting plays in the success of their business.
The first set of regression analyses showed how several
lighting attributes were related to atmosphere dimensions.
The most important attributes were brightness, contrast,
and glare and sparkle. At least one, and sometimes two of
these attributes significantly predicted each of the four
dimensions.
In the second set of regression analyses, context variables
were entered first, before entering the lighting attributes.
This way we minimised the chance of confounds caused by
naturally occurring relationships between interior design
and lighting attributes, which might otherwise lead us to
overestimate light’s contribution to atmosphere perception.
In fact, since the lighting in the shops was also recorded on
the photographs used for the context quantifications, the
present results are probably an underestimation of the
impact of the lighting on perceived atmosphere.
Although some correlations decreased or disappeared,
others remained, showing a consistent contribution for
instance of brightness to the cosy-dimension (the brighter
the impression of the shop, the less confined/intimate/
romantic/relaxing was the atmosphere). Glare and sparkle
added most to liveliness (the more glare and/or sparkle, the
more energising/lively/stimulating was the atmosphere).
Brightness contributed positively to the tenseness
dimension (the more brightness, the more threatening,
tense, uneasy and unfriendly the atmosphere). This was in
fact quite unexpected, and not in line with earlier findings,
which generally relate brightness to more positive
evaluations. This may be specific to this type of
environment and definitely calls for more research. No
specific lighting attribute was related to detachment. This
dimension was largely predicted by the contextual variable
‘legibility’ (running from disorder to order). The more
legible the environment was, the more formal and
businesslike the atmosphere. This same legibility
19
characteristic contributed negatively to the liveliness of the
shop.
Conclusion
This study provides a better understanding of the impact of
lighting on perceived atmosphere in a retail environment.
Lighting attributes and interior qualities were successfully
related to perceived atmosphere. Granted, the amounts of
variance predicted for each of the dimensions of
atmosphere are generally modest, and typically only one of
the lighting attributes had a significant individual
contribution. However, considering the wide variety of
shop interiors, clothing collections, music played et cetera,
we nonetheless consider the findings striking and
encouraging for light designers and researchers: even in the
enormous set of visual environmental cues present in retail
environments, lighting does play a significant role in
creating an ambiance.
ACKNOWLEDGMENTS
We thank the lighting experts of Philips Lighting for
cooperating in developing and performing the lighting
questionnaire.
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