This article appeared in a journal published by Elsevier. The attached
copy is furnished to the author for internal non-commercial research
and education use, including for instruction at the authors institution
and sharing with colleagues.
Other uses, including reproduction and distribution, or selling or
licensing copies, or posting to personal, institutional or third party
websites are prohibited.
In most cases authors are permitted to post their version of the
article (e.g. in Word or Tex form) to their personal website or
institutional repository. Authors requiring further information
regarding Elsevier’s archiving and manuscript policies are
encouraged to visit:
Author's personal copy
The effect of weather on consumer spending
Kyle B. Murray
, Fabrizio Di Muro
, Adam Finn
, Peter Popkowski Leszczyc
School of Business, University of Alberta, Edmonton, Canada AB T6G 2R6
Faculty of Business and Economics, University of Winnipeg, Canada
There has been a great deal of anecdotal evidence to suggest that weather affects consumer decision
making. In this paper, we provide empirical evidence to explain how the weather affects consumer
spending and we detail the psychological mechanism that underlies this phenomenon. Speciﬁcally, we
propose that the effect of weather – and, in particular, sunlight – on consumer spending is mediated by
negative affect. That is, as exposure to sunlight increases, negative affect decreases and consumer
spending tends to increase. We ﬁnd strong support for this prediction across a series of three mixed
methods studies in both the lab and the ﬁeld.
&2010 Elsevier Ltd. All rights reserved.
Weather seems to inﬂuence human behavior in a variety of
ways. Sometimes, weather inﬂuences general behavior. This
occurred when Hurricane Katrina forced the temporary abandon-
ment of New Orleans in 2005. Other times, weather inﬂuences
speciﬁc consumption behaviors. For instance, the type of clothing
we wear depends on the weather—e.g., we wear warmer clothing
in the winter and cooler clothing in the summer.
Building on this type of anecdotal evidence, research has found
that weather variables can affect human behavior. For instance,
research in ﬁnance suggests that the weather may affect stock
returns (Saunders, 1993; Trombley, 1997; Hirshleifer and Shum-
way, 2003; Goetzmann and Zhu, 2005) and that this effect may be
attributed to the inﬂuence that weather has on mood (Cao and
Wei, 2005; Kamstra et al., 2003). Similarly, research exploring the
link between weather and a social activity has reported that
higher temperatures are correlated with increases in violent
assaults and homicides (Cohn, 1990a, 1990b). Researchers have
also found that the number of suicides rise with increases in
barometric pressure and with decreases in wind (Barker et al.,
1994; Stoupel et al., 1999). In addition, results from several
laboratory studies show that artiﬁcial sunlight reduces seasonal
affective disorder (SAD) symptoms for the majority of SAD and
non-SAD depressed participants (Kripke, 1998; Stain-Malmgrem
et al., 1998).
Although the inﬂuence of weather on behavior has been
explored in ﬁelds such as ﬁnance and psychology, it has been
largely ignored in the marketing literature. However, there is
anecdotal evidence that ﬁrms incorporate weather variables into
models that they use to predict sales. For example, Wal-Mart
lowered its June 2006 sales forecasts because unusually cool
summer weather adversely affected sales of air conditioners, as
well as swimming pool supplies. Coca-Cola developed vending
machines that dynamically alter the price consumers are charged
for the soft drink based on changes in the ambient tempera-
ture—i.e., the vending machines increase the price of a soda as the
weather gets hotter (King and Narayandas, 2000).
Nevertheless, the effect of weather on consumer spending has
received only limited attention in the marketing literature (Parker
and Tavassoli, 2000; Parsons, 2001; Steele, 1951). Our work
differs from prior studies as we employ a mixture of methods and
types of data to investigate this issue. This approach is consistent
with Winer (1999), which argues that it is necessary for theory
application research in consumer behavior to establish both
internal and external validity. It is important to not only establish
how variables inﬂuence consumer behavior in an artiﬁcial
laboratory setting, but also to determine whether these variables
inﬂuence behavior in an actual retail setting. In addition, the
research reported in this paper is the ﬁrst to go beyond
demonstrating an effect of weather on consumer behavior to
propose and test the psychological mechanism (i.e., negative
affect) through which a speciﬁc weather variable (i.e., sunlight)
affects consumer spending. Importantly, we ﬁnd that only
negative affect mediates the effect of weather on spending (i.e.,
changes in positive affect do not impact spending).
Our work begins with an analysis of daily sales data, which
establishes an effect of weather on consumer spending at one
independent retail store. Building on the results of the ﬁrst study,
Contents lists available at ScienceDirect
journal homepage: www.elsevier.com/locate/jretconser
Journal of Retailing and Consumer Services
0969-6989/$ - see front matter &2010 Elsevier Ltd. All rights reserved.
Corresponding author. Tel.: +1 780 248 1091.
E-mail addresses: firstname.lastname@example.org (K.B. Murray),
email@example.com (F. Di Muro), adam.ﬁnn@ualberta.ca (A. Finn),
firstname.lastname@example.org (P. Popkowski Leszczyc).
Tel.: +1 780 492 5369.
Tel.: +1 780 492 1866.
Journal of Retailing and Consumer Services 17 (2010) 512–520
Author's personal copy
we investigate the effect of weather, and in particular, sunlight, on
participants’ moods and consumption using panel data. The third
study uses a laboratory experiment to directly test the causal
chain predicted by our theoretical model. We ﬁnd strong support
for the theory that the effect of weather – and, in particular,
sunlight – on consumer spending is mediated by negative affect.
In the next section, we review the literature in three relevant
areas: the inﬂuence of weather on consumer spending, the
inﬂuence of weather on mood and the inﬂuence of mood on
consumer spending. We then describe the three studies we
conducted, along with their results. We conclude with a general
discussion of our ﬁndings.
2. Theoretical background
The extant literature has identiﬁed three general categories of
effects that weather can have on consumer behavior. The ﬁrst is
relatively straightforward: bad weather keeps people at home. In
particular, rain, snow and extreme temperatures have been
identiﬁed as factors that can make going out to shop less
attractive and, thereby, negatively affect both sales and store
trafﬁc (Parsons, 2001; Steele, 1951).
A second set of effects inﬂuence both sales volume and store
trafﬁc in particular product categories (Agnew and Thornes, 1995;
Fox, 1993). For example, when temperatures fall, ice cream sales
decrease, while sales of oatmeal porridge increase (Harrison,
1992). Similarly, people tend to purchase more clothing and
footwear in the winter and more food and drinks in the summer
(Agnew and Palutikof, 1999; Roslow et al., 2000). Retailers
themselves are aware of such effects and use weather as a cue
to begin and end merchandising seasons (Cawthorne, 1998). For
example, gardening supplies begin to appear on store shelves
with the arrival of spring weather, while the sale of snow shovels
coincides with the onset of winter. In general, these studies point
out that some products are better suited to, or even designed for,
particular types of weather.
More interestingly, it has been suggested that weather can
inﬂuence sales by affecting consumers’ internal states. Although
there is very little research that directly addresses this third
category of effects, a few studies have provided preliminary
support for this idea. For example, Parker and Tavassoli (2000)
present a global climate-based model of the effect of weather on
consumer behavior, which predicts variation in consumption
patterns in response to different temperatures and exposure to
sunlight. They argue that consumers do adapt to changes in the
environment by modifying their purchasing behavior to both
maintain physiological homeostasis and to achieve optimal
stimulation levels. Of particular relevance to the current research
is the authors’ suggestion that consumers adapt to lower levels of
sunlight, by consuming stimulants such as alcohol, coffee and
cigarettes. Based on these previous ﬁndings, we predict that:
H1. Weather variables and, sunlight in particular, affect con-
Moreover, we go beyond this basic prediction, and extend the
nascent stream of research that has examined the impact of
weather on consumer behavior, by proposing and testing the
following theoretical model: the effect of weather – and, in
particular, sunlight – on consumer spending is mediated by mood.
In the sections that follow we build on the work cited above,
which indicates that the weather can affect sales, and we brieﬂy
review research that has established links between weather and
mood, as well as between mood and consumer spending. We
conclude our literature review with a section on the mediating
role that mood has been shown to play in the effect of weather on
behavior and extend those studies to predict that mood also
mediates the effect of weather on consumer spending.
2.1. Inﬂuence of weather on mood
Overall, substantial research in psychology has conﬁrmed that
weather can inﬂuence an individual’s mood. For instance, Per-
singer and Levesque (1983) examined the effects of temperature,
relative humidity, wind speed, sunshine hours, barometric
pressure, geomagnetic activity and precipitation on a unidimen-
sional mood rating scale. They found that 40% of mood evalua-
tions were accounted for by a combination of meteorological
events; in particular, barometric pressure and sunshine had the
strongest impact on mood. Other researchers employing varying
mood scales have found that low levels of humidity (Sanders and
Brizzolara, 1982), high levels of sunlight (Cunningham, 1979;
Parrott and Sabini, 1990; Schwarz and Clore, 1983), high
barometric pressure (Goldstein, 1972) and high temperature
(Cunningham, 1979; Howarth and Hoffman, 1984) are associated
with positive mood. Research has also found that weather’s
psychological inﬂuences are moderated by the season and the
amount of time spent outside (Keller et al., 2005).
In addition to studies reporting an effect of weather on positive
affect, research has shown that weather can also impact negative
affect. In particular, exposure to sunlight improves peoples’ mood
by reducing negative affect. This effect appears to be associated
with the production of serotonin in the human brain. Speciﬁcally,
the rate of serotonin production is directly related to the length of
exposure to sunlight, and rises rapidly with increased exposure to
sunlight (Lambert et al., 2002). Artiﬁcial sunlight is also able to
improve mood by reducing negative affect. Controlled laboratory
studies have shown that artiﬁcial sunlight – produced, for
example, by ‘‘sun lamps’’ – can improve mood and diminish
SAD symptoms for both SAD and non-SAD depressed patients
(Kripke, 1998; Stain-Malmgrem et al., 1998). Other studies
utilizing artiﬁcial sunlight indicate that such lighting improves
mood and vitality among non-depressed individuals (Leppamaki
et al., 2002, 2003). This leads us to predict that with regards to
mood sunlight is a particularly important weather variable and
that it has its primary effect on the negative dimension of affect.
H2. Exposure to sunlight reduces negative affect.
2.2. Inﬂuence of mood on consumer spending
According to Gardner (1985), mood is as a phenomenological
property of an individual’s perceived affective state. In addition,
she argues that moods are mild, transient, general and pervasive
states that may be particularly inﬂuential in retail or service
encounters, because of their interpersonal or dyadic nature.
Empirical research suggests that people in positive moods are
more likely to evaluate consumer goods (e.g., cars, TVs, etc.) more
favorably than those in neutral moods (e.g., Bitner, 1992; Isen
et al., 1978; Obermiller and Bitner, 1984). Prior work has also
demonstrated that people in a positive mood are more likely to
self-reward and tend to spend more money (Golden and Zimmer-
man, 1986; Sherman and Smith, 1987; Underwood et al., 1973).
In a store intercept study, Donovan et al. (1994) examined the
relationship between shoppers’ emotional states and their actual
in-store spending. They found that more positive states resulted
in greater overall spending. In related research, Spies et al. (1997)
proposed that store atmospheric variables affect consumers’
moods, which in turn affects their purchasing behavior. The
authors compared the effects of two IKEA stores that differed in
terms of their atmosphere (i.e., layout, interior colors, recency of
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520 513
Author's personal copy
renovations, presentation of furniture, etc.). One of the stores,
described as ‘‘pleasant,’’ was rated as more attractive and
appealing on these dimensions than the other (‘‘unpleasant’’)
store. They measured the effects of these differences on
consumers’ mood and spending. Their results indicate that
consumers’ moods improved during their time in the store with
the more pleasant atmosphere. In addition, they found that
customers’ moods had a direct effect on how much money they
tended to spend—that is, people in more positive moods spend
These prior ﬁndings suggest that consumers in a better mood
will tend to spend more money. We recognize that a consumer’s
mood can be improved by increasing positive affect or by
decreasing negative affect, but prior research has not differen-
tiated between these two distinct mechanisms. Given that we
expect sunlight to reduce negative affect, we more speciﬁcally
H3. As negative affect decreases, consumer spending increases.
2.3. The mediating effect of mood
Previous research has demonstrated that weather inﬂuences
behavior and that mood can mediate such effects (e.g., Barker
et al., 1994;Cao and Wei, 2005;Cohn, 1990a, 1990b;Cunning-
ham, 1979;Kamastra et al., 2003). The literature reviewed above
provides support for the effect of weather on mood, the effects of
mood on consumer spending, and the effect of weather on
consumer spending. In addition, of the weather variables that
have been studied, sunlight appears to play a particularly
important role in improving mood (Keller et al., 2005; Kripke,
1998; Lambert et al., 2002; Leppamaki et al., 2003; Stain-
Malmgrem et al., 1998). Speciﬁcally, both natural and artiﬁcial
sunlight are able to improve mood by reducing negative affect.
Similarly, the little research examining the relationship between
weather and consumer spending suggests that sunlight is also an
important factor in consumption decisions (e.g., Parker and
Tavassoli, 2000). Therefore, as illustrated in Fig. 1, we predict
that negative (but not positive) affect plays an important
mediating role in the relationship between sunlight and consumer
H4. Negative affect mediates the effect of sunlight on consumer
We next present the results of three studies that examine the
relationship between weather and consumer spending. Each
study employs a different method in an attempt to triangulate
the effect of weather on consumer spending with daily sales data,
panel data and a laboratory experiment. The ﬁrst study estab-
lishes the effect that weather can have on consumer spending by
examining the correlation between a wide variety of weather
variables and the daily sales of a small independent retailer. The
results show that snow fall, humidity and sunlight all have
signiﬁcant effects on consumer spending. The second study
focuses on correlations between weather variables and panel
data, recorded by individual consumers in a daily diary, which
captures consumption patterns and measures ﬂuctuations in
mood (positive affect and negative affect) over twenty days. We
ﬁnd that sunlight inﬂuences mood (negative affect), which
subsequently affects consumption. The third study manipulates
(artiﬁcial) sunlight in a laboratory setting. The results of this study
conﬁrm that negative affect can mediate the effect of sunlight on
consumer spending decisions. Speciﬁcally, we ﬁnd that partici-
pants exposed to artiﬁcial sunlight are willing to pay signiﬁcantly
more for a variety of products than participants exposed to
regular lighting only, and that this effect is mediated by negative
3. Study 1
The primary objective of study 1 was to test the general
premise that weather variables can affect consumer spending. We
wanted to see if and how weather might inﬂuence daily sales in a
retail setting. In this study, we analyze secondary sales data from
one independent retail store located in a large North American
city. The store specialized in a single product line: tea and related
Data: Our data consist of six years of daily sales and daily
weather variables. The dependent variable in our model is the
store’s total daily sales. Our independent weather variables are:
temperature (minimum, maximum and average), rain fall, snow
fall, dry bulb, which is a measure of air temperature measured by
a thermometer freely exposed to the air but shielded from
radiation and moisture (minimum, maximum and average),
humidity (minimum, maximum and average), wind direction,
wind speed (minimum, maximum and average), barometric
pressure (minimum, maximum and average) and sunlight. In
addition, we controlled for season, the month, the day of the
week, whether or not the store was open, and whether or not it
was a holiday.
Model used: To test hypothesis 1, we estimated a random
effects model, with the log of daily sales as the dependent variable
(see below). A log transformation was used to normalize the sales
data. This model has a random intercept to control for differences
in sales across month and day (where day is treated as nested
is the log of daily tea sales in month iand day j; for
i¼1, y, 12, j¼1, y,7;a
is the intercept for month iand day j;
Temp is the average temperature for the day; Snow is the total
snow fall during the day; Sun is the total hours of sunshine for the
is the quadratic term for the average temperature for
the day; e
is a random error term.
Fig. 1. The mediating role of negative affect in the effect of sunlight on consumer spending.
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520514
Author's personal copy
The results are reported in Table 1. Consistent with hypothesis
1, we found that several weather variables had a signiﬁcant effect
on daily sales in this store over the six year time period.
Speciﬁcally, we found main effects for average temperature
¼0.042; t¼6.81; po0.001), snow fall (b
t¼2.11; p¼0.035), sunlight (b
¼0.259; t¼3.69; po0.001)
and a main effect for humidity (b
¼0.010; t¼4.24; po0.001).
We also found an interaction effect between average temperature
and sunlight (b
¼0.029; t¼5.08; po0.001) such that the effect of
sunlight on sales is positive at lower temperatures and negative at
higher temperatures. In addition, there is a nonlinear effect of
temperature on sales. Speciﬁcally, there is a negative linear effect
¼0.042; t¼6.81; po0.001) and a positive quadratic effect
¼0.0002; t¼4.86; po0.001) of temperature on sales, suggest-
ing that sales go up as temperature goes down but this effect on
sales diminishes as temperatures become lower.
The results are compatible with previous research, which has
found three general categories of weather effects. For example,
consistent with the effects from the ﬁrst category – i.e., bad
weather can make going out to shop less attractive – we ﬁnd that
when it snows sales decrease. We also ﬁnd effects that may be
product speciﬁc—that is, sales of tea (and related accessories)
decline when the weather is warmer and more humid. However,
Persinger (1975) found that both humidity and precipitation can
contribute to a negative mood and, therefore, these effects may be
Similarly, the results of study 1 indicate that the effect of
sunlight, is conditional upon the average temperature—that is,
the effect is captured by the interaction, which indicates that
when temperatures are low, increased sunlight has a positive
effect on tea sales. Although sunlight has been identiﬁed as a key
variable in previous studies of the impact of weather on mood
(Cunningham, 1979; Parrott and Sabini, 1990; Schwarz and Clore,
1983; Kripke, 1998; Stain-Malmgrem et al., 1998; Leppamaki
et al., 2002, 2003; Lambert et al., 2002), the direction of this effect
is consistent with both a product speciﬁc category explanation
and a reduction in negative affect story. When it is already warm,
higher levels of sunlight decrease tea sales.
Supporting hypothesis 1, study 1 provides strong evidence that
weather can affect sales. However, the secondary data used in this
study lacks measures of consumer mood, which are required to
test hypotheses 2 through 4. In study 2, we use daily panel data to
focus on the relationship between weather and mood, as well as
the relationship between mood and consumption.
4. Study 2
Participants: This study utilized 33 participants who were
recruited from the general population of students at a large North
American university. Participants were paid $100 to provide daily
panel data by completing a web survey at the end of each day for
twenty days in the month of March (average daily high 361F).
Data: The daily survey included structured questions to
measure participants’ mood, spending on and consumption of
tea and coffee, as well as individuals’ total expenditures for the
day. Building on study 1, this study adds a new product category
(coffee) and in addition to measuring dollars spent we also ask
participants for information on their actual consumption beha-
viors (i.e., not just how much they spend on tea and coffee, but
how many cups of each beverage they drink). Respondents
reported their mood using the PANAS scale (Watson et al.,
1988) to capture positive and negative affect. The weather
variables recorded during the collection of the panel data include
the daily averages for temperature, humidity and barometric
pressure, as well as the total hours of sunshine for the day.
To test the prediction that weather, and sunlight in particular
(H2), affect mood, factor scores for positive and negative affect
were estimated using the pooled PANAS data. Then, the factor
scores for positive affect and negative affect were each regressed
on the daily weather variables, resulting in one model with
positive mood as the dependent variable and one with negative
mood as the dependent variable
is the factor score for positive affect or negative affect for
panel member ion day of the week j;a
is the intercept for panel
member iand day of the week j;Temp
is the average temperature
for the day of the week j;Sun
is the total hours of sunshine for the
day of the week j;Humid
is the average humidity for the day of
the week j;Pressure
is the average barometric pressure for the
day of the week j;e
is a random error term for panel member ion
day of the week j.
Consistent with hypothesis 2, increased sunlight reduced
negative affect (b
¼0.042; t¼2.04; p¼0.042). We also found
that increased humidity reduced positive affect (b
t¼2.02; p¼0.044). No other effects of weather on mood were
present in this data. The full results are reported in Table 2aandb.
Only 25 purchases of tea or coffee were recorded over the 20
day period of the panel data. As a result of this small number of
observations, we were unable to test hypothesis 1, 3 and 4 (i.e.,
the effects of weather and mood on consumer spending) with this
data. Therefore, our analysis focuses on consumption patterns for
which we have sufﬁcient data points. Speciﬁcally, we ran two
regression models with consumption (i.e., the cups of tea or coffee
consumed per day) as the dependent variable and the mood
variables (i.e., positive and negative affect factor scores) as the
independent variables. We ﬁnd no effect of mood on coffee
¼0.002, t¼0.060, p¼0.950; PA:
t¼0.870, p¼0.387); however, negative affect does have a
signiﬁcant positive impact on tea consumption (NA:
t¼2.020, p¼0.040). The effect of positive affect on tea
Study 1—results of the random effects model.
Variables Coefﬁcients t-values p-values
Intercept 5.560 9.89 o0.001
Temperature 0.042 6.81 o0.001
Snow fall 0.042 2.11 0.035
Sunlight 0.259 3.69 o0.001
Humidity 0.010 4.24 o0.001
Sunlight Temperature 0.029 5.08 o0.001
Temperature Temperature 0.0002 4.86 o0.001
We used the Bayesian Information Criteria to determine which interaction
and quadratic terms to include in the model.
We tested for non-linearity of the weather related variables; however, no
signiﬁcant quadratic effects were found.
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520 515
Author's personal copy
consumption was not signiﬁcant (PA:
p¼0.387) (Table 3).
Study 2 uses consumer panel data that includes measures of
mood, and thus, addresses a limitation of study 1. Speciﬁcally, the
key ﬁnding of study 2 is that sunlight reduces negative affect,
which supports hypothesis 2 and replicates the effect of sunlight
on negative affect that has been documented in prior research
(Kripke, 1998; Stain-Malmgrem et al., 1998). In addition, we ﬁnd
that humidity decreases positive affect, which is also consistent
with prior work (Sanders and Brizzolara, 1982).
Spending on tea and coffee was unexpectedly too infrequent in
this data set to allow us to test hypotheses 1, 3 and 4.
Interestingly, however, we did ﬁnd that as negative affect
increased the consumption of tea also increased. In study 1,
although we did not measure mood, the results were consistent
with the expected mood congruency effect—that is, people tend
to buy more tea when they were in a better mood. In study 2, we
observe what appears to be a mood regulation effect—that is,
people tend to drink more tea when their mood is worse (i.e.,
negative affect is higher). Study 3 allows us to more directly test
the impact that sunlight and mood have on spending in a
controlled laboratory environment. We discuss our results in
terms of these two types of effects – i.e., mood regulation and
mood congruency – in more detail in the general discussion.
Overall, the results of study 2 provide further support for our
theoretical model and, in particular, the critical link between
sunlight and negative affect (H2). However, the panel data has its
own limitations. First, these data were collected in an environ-
ment where signiﬁcant noise was present. Second, the measure of
mood was only recorded once at the end of the day and not at the
time of the spending or consumption decisions. Third, the lack of
spending data did not allow us to test hypotheses 1, 3 and 4.
Fourth, because exposure to sunlight was not manipulated, we
cannot claim strong support for the causal effect on mood
predicted by our model (Fig. 1). Nevertheless, studies 1 and 2
provide converging evidence that is consistent with our model
using two different data sets that were developed with two
distinct methods. In study 3, we again test the propositions of our
model, this time using a third method (i.e., a laboratory
experiment). Importantly, study 3 allows us to directly test
hypothesis 4 – i.e., negative affect mediates the effect of sunlight
on consumer spending – in a controlled environment where
exposure to (artiﬁcial) sunlight is manipulated and mood is
measured at the time the spending decision is made.
5. Study 3
Studies 1 and 2 indicated that sunlight is the weather variable
that appears to have the predominant effect on both mood (i.e.,
negative affect) and consumer spending. Therefore, study 3
focuses on sunlight and manipulates participants’ exposure to
artiﬁcial sunlight using a specially designed ‘‘sun lamp.’’ In
addition, study 3 extends the product categories that are
investigated beyond tea to include a variety of common consumer
products (i.e., orange juice, a one-month gym membership, an
airline ticket and a one-month newspaper subscription). We
measure positive and negative affect after exposure to the
artiﬁcial sunlight and immediately before participants express
their willingness-to-pay for each of these products.
Participants: This study was completed by 78 students at a large
North American university. Five participants were removed because
they were identiﬁed as outliers: their willingness to pay for a
product was greater than three standard deviations from the mean.
Procedure: In this experiment, sunlight was manipulated with
a sun lamp in a between-subjects design. The sun lamp was a desk
lamp that was designed to produce light very similar in wave
length to natural sunlight. Participants were randomly assigned to
either a room containing a sun lamp, or to a room without a sun
lamp. The sun lamp’s location was counterbalanced between the
two rooms—i.e., it was located in each room for approximately
half of the time. This was done to control for the effects of any
potential particularities associated with the two rooms.
Once participants were randomly assigned to an experimental
condition, they were asked to read a short document (a review of
English literature written during the time period from 1660 to
1689). Reading this document took, on average, 20 min. Partici-
pants were then asked to complete the PANAS mood scale and
ﬁnally responded to open ended questions eliciting their will-
ingness to pay for ﬁve products: green tea, juice, a gym
membership, an airline ticket and a newspaper subscription.
Data: The dependent variable was measured by asking
participants how much they would pay for a certain quantity of
the product in question. Speciﬁcally, participants were asked how
much they would be willing to pay for (1) 24 tea bags of Lipton’s
Green Tea; (2) a 2L carton of orange juice; (3) an one-month
gym membership; (4) an airline ticket; and, (5) an one-month
Study 2—the inﬂuence of weather on sales.
Coefﬁcients t-values p-values
Number of cups
Intercept 0.792 0.94 0.347
Temperature 0.0002 0.06 0.952
Sunlight 0.006 0.62 0.539
Humidity 0.034 0.10 0.924
Pressure 0.001 1.21 0.225
Tea volume Intercept 8.775 0.54 0.589
Temperature 0.037 0.60 0.552
Sunlight 0.009 0.05 0.961
Humidity 2.746 0.40 0.687
Pressure 0.105 0.69 0.488
Coffee Intercept 1.389 1.20 0.233
Temperature 0.002 0.41 0.679
Sunlight 0.014 1.13 0.259
Humidity 0.128 0.26 0.793
Pressure 0.016 1.50 0.135
Variables Coefﬁcients t-values p-values
(a) Study 2—the inﬂuence of weather on positive affect
Intercept 3.032 1.91 0.056
Temperature 0.002 0.35 0.729
Sunlight 0.018 0.99 0.322
Humidity 1.40 2.02 0.044
Pressure 0.021 1.37 0.173
(b) Study 2—the inﬂuence of weather on negative affect
Intercept 0.041 0.02 0.983
Temperature 0.008 1.04 0.297
Sunlight 0.042 2.04 0.042
Humidity 0.007 0.37 0.710
Pressure 0.913 1.14 0.256
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520516
Author's personal copy
newspaper subscription. These products were chosen because
they are thought to be relevant to the student participants. In all
cases, participants’ mood was assessed using the PANAS mood
scale (Watson et al., 1988), which provides measures of both
positive and negative affect.
First, consistent with hypothesis 1, we ﬁnd that sunlight has a
signiﬁcant positive effect on willingness-to-pay (see Table 4) for
all ﬁve products. Second, consistent with hypothesis 2, we ﬁnd
that sunlight has a signiﬁcant negative effect on negative affect
¼2.94; t¼2.99, p¼0.004), but no signiﬁcant effect on positive
¼0.57; t¼0.35, p¼0.731).
Next we test the effect of negative affect on spending (H3) and
the predicted mediating role of negative affect in the relationship
between sunlight and willingness to pay (H4). The results
reported in Table 5 provide strong support for hypotheses 3 and
4. The results indicate that for all ﬁve products the effect of
sunlight on willingness to pay is mediated by negative affect
(Baron and Kenny, 1986). Moreover, in all cases the mediation is
partial, as the effect of sunlight on willingness to pay is still
signiﬁcant after controlling for negative affect. However, the size
of the coefﬁcient for sunlight is substantially reduced after
controlling for negative affect.
As recommended by Winer (1999), we have employed three
different methods and types of data in an attempt to triangulate
the effects of weather on consumer spending and to establish the
external validity of ﬁndings from our laboratory experiment. Our
experimental design allows us to demonstrate a cause-and-effect
relationship between exposure to sunlight and an increased
willingness to pay for common products. This ﬁnding builds on
and complements the results of studies 1 and 2. In addition, study
3 extends our results to ﬁve products categories, all of which
provide strong support for our model.
6. General discussion
The results of the studies reported in this paper provide
evidence of how weather can impact consumer spending. We ﬁnd
that temperature, humidity, snow fall, and, especially sunlight,
can affect retail sales. In addition, the panel data replicate the
general result of previous research, which found that sunlight
affects mood (Cunningham, 1979; Parrott and Sabini, 1990;
Schwarz and Clore, 1983), while simultaneously demonstrating
that reductions in negative affect are associated with higher levels
of consumption and spending. Also, we found a causal effect of
sunlight on willingness to pay and demonstrated that the effect
was mediated by negative affect.
Our ﬁnding that the effect of sunlight on consumption is
mediated by negative affect is an important extension of prior
theories that found a more positive mood facilitates spending
(Donovan et al., 1994; Golden and Zimmerman, 1986; Sherman
and Smith, 1987; Spies et al., 1997; Underwood et al., 1973).
Speciﬁcally, we ﬁnd that although some weather variables such as
humidity may have an impact on mood through positive affect,
only negative affect has an effect on consumer spending. This
result provides further insight into the underlying psychological
mechanism and, based on prior SAD research (Lambert et al.,
2002), suggests a possible neuro-chemical basis for this effect (i.e.,
serotonin). This opens the door for future research to dig deeper
into the speciﬁc link between weather based changes in mood and
This research also contributes to the literature on the inﬂuence
of store atmosphere on consumer shopping behavior. Regarding
store atmosphere, research by Kotler (1973) indicates that
consumers respond to the ‘‘total product’’, and that a signiﬁcant
component of the total product is the place where the product is
bought or consumed. In fact, the store atmosphere could be more
Study 3—willingness to pay for products in sunlight and no sunlight conditions.
Products Sunlight condition
(mean willingness-to-pay in $)
No sunlight condition
(mean willingness-to-pay in $)
Green tea 4.61 3.35 2.36 0.021
Orange juice 3.51 2.90 2.19 0.032
Gym membership 41.67 32.89 2.07 0.042
Airline ticket 517.98 400.00 2.20 0.031
Newspaper subscription 17.79 11.41 2.30 0.024
Study 3—results of Barron and Kenny (1986) and Sobel (1982) mediation tests.
Products Sunlight on WTP for
NA on WTP for
PA on WTP for
Impact of sunlight on WTP
controlling for NA
Green tea 1.26
Orange juice 0.62
Gym membership 8.78
Airline ticket 117.98
Numbers in the table represent beta-coefﬁcients; for the Sobel test, numbers represent z-values.
NA refers to negative affect; PA refers to positive affect.
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520 517
Author's personal copy
inﬂuential than the product itself in the purchase decision (Kotler
1973, p. 48). Lighting is considered to be an important component
of the store atmosphere, as a more appealing store with better-
illuminated merchandise could entice shoppers to visit the store,
linger, and perhaps even make a purchase (Summers and Hebert,
In the store atmosphere literature, the dominant theoretical
model, the Mehrabian and Russell (1974) (M–R) model of
approach–avoidance behavior, posits that the combined effects
of pleasure, arousal and dominance inﬂuences people’s behaviors
in shopping environments. Regarding lighting, the M–R (1974)
model theorizes that brighter lighting increases pleasantness and
arousal, and that the combination of pleasantness and arousal will
positively inﬂuence consumers’ shopping behaviors. Although few
empirical lighting studies have been conducted (Areni and Kim,
1994; Summers and Hebert, 2001), these studies have supported
the M–R (1974) model of approach–avoidance behavior. For
instance, Areni and Kim (1994) studied the impact of in-store
lighting on shopping behavior utilizing a sample of 171 wine store
consumers over a 16-night period. Lighting was manipulated to
be ‘‘soft’’ on eight different evenings by replacing some of the
store’s existing lamps with lower-wattage lighting. On the eight
remaining evenings, lighting was manipulated such that it was
‘‘bright’’ by replacing lamps with higher-wattage lighting. Results
of this research show that consumers examined and handled
signiﬁcantly more items under ‘‘bright’’ lighting conditions than
under ‘‘soft’’ lighting conditions.
Summers and Hebert (2001) tested the inﬂuence of lighting on
consumers’ approach behavior by installing supplemental lighting
in two hardware stores. The lighting treatment was alternated
each Friday and Saturday for 8 h/day per display. The results of
this study indicate that lighting inﬂuences consumer approach
behavior, as consumers touched, and picked up more items when
additional lighting was present. In addition, consumers spent
more time at displays under the on treatment than the off
treatment. Overall, the results of these studies suggest that the
observed effect of lighting on consumer behavior is attributed to
arousal and pleasure. However, our results suggest a mechanism
not captured by the M–R (1974) model. Speciﬁcally, we ﬁnd that
the mitigation of negative affect can explain the positive effect of
lighting on shopping behavior. Furthermore, we show that the
positive inﬂuence of lighting, caused by the mitigation of negative
affect, actually inﬂuences consumer spending, while Summers
and Hebert (2001) and Areni and Kim (1994) do not measure
Managerial implications: The weather is not under manage-
ment’s control; yet, retailers must respond to changes in the
weather on a regular basis. Prior research has demonstrated that
weather can affect store trafﬁc and complicate stafﬁng decisions
(Agnew and Thornes, 1995; Parsons, 2001; Steele, 1951). It can
also drive consumers towards some products and away from
others—e.g., ice cream when its hot and oatmeal when its cold
(Harrison, 1992). In addition, retail distribution networks, which
have been designed for efﬁciency, tend to struggle in the face of
unexpected adverse weather conditions that can range from
relatively minor regional storms to global disruptions from
climate change and volcanic activity (e.g., Koetse and Rietveld,
2009; Prater et al., 2001; Stecke and Kumar, 2009). As a result,
retailers are often forced to respond to the effects of weather in a
reactive, rather than proactive manner.
In this paper, we provide compelling evidence that weather
variables can also affect consumers’ internal states, which then
inﬂuence their spending decisions. Speciﬁcally, we ﬁnd that
sunlight can reduce negative affect that, in turn, increases
consumer spending. In addition, we have demonstrated that such
affects occur with both natural and artiﬁcial sunlight. These
ﬁndings build on prior research – which has demonstrated the
inﬂuence that store atmospheric variables such as scent and
music have on consumer spending (e.g., Bruner, 1990; Morrison
et al., forthcoming) – and imply that one key weather variable
may be proactively managed by retailers. For example, our results
suggest that retail stores could selectively increase lighting levels
on bad weather days in order to reduce negative feelings, which,
in turn, should help increase sales. When the weather is already
good, consumers’ negative feelings will already tend to be low.
In addition, our results suggest that stores incorporate natural
lighting (i.e. daylight) and/or alter their lighting such that it
closely resembles sunlight, in order to reduce consumers’
negative affect and increase sales. Such greater use of natural
lighting has beneﬁts for employees (Edwards and Torcellini, 2002)
and should also lead to signiﬁcant cost savings. In fact, for some
buildings, over 90% of lighting energy consumed can be an
unnecessary expense of excessive illumination (Hawken, 2000).
Thus, turning off some electric lights when sufﬁcient daylight is
available should help save on lighting energy costs. Recent
research has shown that daylight can introduce large energy
savings in single-story commercial buildings, especially when it
enters through the top of the building (Hesong et al., 2002).
Furthermore, because daylight introduces less heat into a building
than the equivalent amount of electric light, cooling costs can be
Limitations and directions for future research: One limitation of
our work is that study 2 and study 3 were both conducted during
the cooler half of the year. The main effects of sunshine which we
observed might not generalize to other times of the year when
temperatures are warmer and negative affect is less prevalent.
Indeed, we found an interaction effect between temperature and
sunlight in study 1, which suggests that the effect of more
sunlight on retail sales becomes negative when the weather is
already warm (e.g., during the summer). Second, the evidence
that negative affect mediates the effect of weather on consumer
behavior is only available for sunshine. However, our study 1
analysis of the retail stores’ sales found other effects of weather
variables that might also be accounted for by their effects on
mood. The research on weather effects has used various measures
of mood, but it includes some results that are consistent with the
negative effects we found for humidity and snow fall (precipita-
tion) on retail sales. Additional research, is needed to examine
whether the effects of these weather variables on consumer
behavior are also mediated by mood.
Similarly, our predictions were motivated by a stream of
research that has found that as consumers’ moods become more
positive, they spend more money (Spies et al., 1997; Underwood
et al., 1973). In studies 1 and 2, we ﬁnd that as sunlight reduces
negative affect – and thus consumers’ moods become more
positive – consumers do tend to be willing to spend more. In
study 2, however, we ﬁnd that lower negative affect is correlated
with more cups of tea being consumed. This ﬁnding is consistent
with prior work on mood regulation (Bruyneel et al., 2009; Kivetz
and Kivetz, 2008). For example, in contrast to the work cited
above, prior research has found people in a negative mood tend to
self-gratify or self-reward through consumption and purchasing
more than controls (Thayer et al., 1994; Hadjimarcou and Marks,
1994; Gardner and Scott, 1990; Garg et al., 2007).
Recently, Kivetz and Kivetz (2008) have proposed that these
conﬂicting results can be explained by two distinct mood
mechanisms: (1) mood congruency, which states that people
respond in accordance with their mood; and, (2) mood regulation,
which states that people try to manage their mood. They argue
that the psychological distance between individuals and the
consequences of their actions and decisions is an important
moderator of the impact of mood. Speciﬁcally, they contend that
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520518
Author's personal copy
mood congruency is most likely to be observed in decisions with
psychologically distant outcomes, while mood regulation is more
likely to occur when outcomes are proximal to the self and easy to
experience. Two of the studies reported in this paper focused on
psychologically distant outcomes—that is, willingness to pay
(study 3) and the purchase of products to be consumed in the
future (study 1). The results of both of these studies are consistent
with mood congruency. In study 2, which looked at the
psychologically proximate consumption of tea and the results
were consistent with mood regulation. Additional research is
required to improve our understanding of the opposing nature of
these two types of effects.
Finally, future research should also investigate the relationship
between weather, mood and the effectiveness of in-store promo-
tional activities. One possibility is that promotional activities that
temporarily reduce margins are less necessary when the weather
is good and customers already have lower levels of negative
affect, which increases willingness-to-pay.
The authors would like to thank the tea company that provided
the data for Study 1 and acknowledge the support of the
University of Alberta’s School of Retailing and the Bannister Chair
in Marketing held by the third author.
Agnew, M.D., Palutikof, J.P., 1999. The impacts of climate on retailing in the UK
with particular reference to the anomalously hot summer of 1995. Interna-
tional Journal of Climatology 19 (November), 1493–1507.
Agnew, M.D., Thorne, J.E., 1995. The weather sensitivity of the UK food retail and
distribution industry. Meteorological Applications 2 (June), 137–147.
Areni, C.S., Kim, D., 1994. The inﬂuence of in-store lighting on consumers’
examination of merchandise in a wine shop. International Journal of Research
in Marketing 11 (March), 117–125.
Barker, A., Hawton, K., Fagg, J., Jennison, C., 1994. Seasonal and weather factors in
parasuicide. British Journal of Psychiatry 165 (3), 375–380.
Baron, R.M., Kenny, D.M., 1986. The moderator–mediator variable distinction in
social psychological research: conceptual, strategic and statistical considera-
tions. Journal of Personality and Social Psychology 51 (6), 1173–1182.
Bitner, M.J., 1992. Servicescapes: the impact of physical surroundings on
customers and employees. Journal of Marketing 56 (April), 57–71.
Bruner, G.C., 1990. Music, mood and marketing. Journal of Marketing 54 (October),
Bruyneel, S.D., Dewitte, S., Hans, F.P., Dekimpe, M.G., 2009. I felt low and my purse
feels light: depleting mood regulation attempts affect risk decision making.
Journal of Behavioral Decision Making 2 (22), 153–170.
Cao, M., Wei, J., 2005. Stock market returns: a note on temperature anomaly.
Journal of Banking & Finance 29 (June), 1559–1573.
Cawthorne, C.P., 1998. Weather as a strategic element in demand chain planning.
The Journal of Business Forecasting Methods and Systems 17 (Fall), 18–21.
Cohn, E.G., 1990a. Weather and crime. British Journal of Criminology 30 (1), 51–64.
Cohn, E.G., 1990b. Weather and violent crime. Environment and Behavior 22
Cunningham, M.R., 1979. Weather, mood, and helping behavior: quasi experi-
ments with the sunshine samaritan. Journal of Personality and Social
Psychology 37 (November), 1947–1956.
Donovan, R.J., Rossiter, J.R., Marcoolyn, G., Nesdale, A., 1994. Store atmosphere and
purchasing behavior. Journal of Retailing 70 (3), 283–294.
Edwards, L., Torcellini, P., 2002. A Literature Review of the Effects of Natural Light
on Building Occupants. (NREL/TP-550-30769). National Renewable Energy
Laboratory, Golden, CO.
Fox, F.D., 1993. Managing the impact of weather. Retail Control 61, 14–18.
Gardner, M.P., 1985. Mood states and consumer behavior: a critical review. Journal
of Consumer Research 12 (December), 281–300.
Gardner, M.P., Scott, John, 1990. Product type: a neglected moderator of
mood. In: Goldberg, M.E., Gorn, G., Pollay, R.W. (Eds.), Advances in Consumer
Research, vol. 17. Association for Consumer Research, Provo, UT, pp. 585–589.
Garg, N., Wansink, B., Inman, J.J., 2007. The inﬂuence of incidental affect on
consumers’ food intake. Journal of Marketing 71 (January), 194–206.
Goetzmann, W.N., Zhu, N., 2005. Rain or shine: where is the weather effect?
European Financial Management 11 (November) 559–578.
Golden, L.G., Zimmerman, D.A., 1986. Relationship between affect, patronage,
frequency and amount of money spent with a comment on affect scaling and
measurement. In: Lutz, R.J., Provo, U.T. (Eds.), Advances in Consumer Research,
vol. 13. Association for Consumer Research.
Goldstein, K.M., 1972. Weather, mood, and internal–external control. Perceptual
and Motor Skills 35 (August), 786.
Hadjimarcou, J., Marks, L.J., 1994. An examination of the effects of context-
inducted mood states on the evaluation of a ‘feel-good’ product: the
moderating role of the product type and the consistency effects model. In:
Allen, C.T., John, Deborah Roedder (Eds.), Advances in Consumer Research, vol.
21. Association for Consumer Research, Provo, UT, pp. 509–513.
Harrison, K., 1992. Whether the weather be good. Super Marketing, 15–17.
Hawken, P., 2000. Imagine: What America Could Be in the 21st Century.
Pennsylvania: Rodale Press.
Hesong, L., Wright, R.L., Okura, S., 2002. Daylighting impacts on retail sales
performance. Journal of the Illuminating Engineering Society 31 (2), 21–25.
Hirshleifer, D., Shumway, T., 2003. Good day sunshine: stock returns and the
weather. Journal of Finance 58 (June), 1009–1032.
Howarth, E., Hoffman, M.S., 1984. A multidimensional approach to the relationship
between mood and weather. British Journal of Psychology 75 (February),
Isen, A.M., Shalker, T.E., Clark, M.S., Karp, L., 1978. Inﬂuence of positive affecton the
subjective utility of gains and losses: it is just not worth the risk. Journal of
Personality and Social Psychology 55, 710–717.
Kamstra, M.J., Kramer, L.A., Levi, M.D., 2003. Winter blues: a SAD stock market
cycle. American Economic Review 93 (1), 324–343.
Keller, M.C., Fredrickson, B.L., Ybarra, O., Cote, S., Johnson, K., Mikels, J., Conway, A.,
Wager, T., 2005. A warm heart and a clear head: the contingent effects
of weather on mood and cognition. Psychological Science 16 (September),
King, C., Narayandas, D., 2000. Coca-cola’s new vending machine (A): pricing to
capture value, or not? Harvard Business School Case # 9-500-068.
Kivetz, R., Kivetz, Y., 2008. Reconciling mood congruency and mood regulation,
working paper, Columbia Business School.
Koetse, M.J., Rietveld, P., 2009. The impact of climate change and weather on
transport: an overview of empirical ﬁndings. Transportation Research Part D:
Transport and Environment 14 (3), 205–221.
Kotler, P., 1973. Atmospherics as a marketing tool. Journal of Retailing 49 (Winter),
Kripke, D.F., 1998. Light treatment for non-seasonal depression: speed, efﬁcacy,
and combined treatment. Journal of Affective Disorders 49 (2), 109–117.
Lambert, G.W., Reid, C., Kaye, D.M., Jennings, G.L., Esler, M.D., 2002. Effects of
sunlight and season on serotonin turnover in the brain. The Lancet 360
Leppamaki, S., Partonen, T., Lonnqvist, J., 2002. Bright-light exposure combined
with physical exercise elevates mood. Journal of Affective Disorders 72
Leppamaki, S., Partonen, T., Piiroinen, P., Haukka, J., Lonnqvist, J., 2003. Timed
bright-light exposure and complaints related to shift work among women.
Scandinavian Journal of Work, Environment, and Health 29 (February), 22–26.
Mehrabian, A., Russell, J.A., 1974. An Approach to Environmental Psychology. MIT
Press, Cambridge, MA.
Morrison, M., Gan, S., Dubelaar, C., Oppewal, H.. In-store music and aroma
inﬂuences on shopper behavior and satisfaction. Journal of Business Research
Obermiller, C., Bitner, M.J., 1984. Store atmosphere: a peripheral cue for product
evaluation. In: Stewart, David C. (Ed.), American Psychological Association
Annual Conference Proceedings, Consumer Psychology Division. American
Psychological Association, pp. 52–53.
Parker, P.M., Tavassoli, N.T., 2000. Homeostasis and consumer behavior across
cultures. International Journal of Research in Marketing 17 (March), 33–53.
Parrott, W.G., Sabini, J., 1990. Mood and memory under natural conditions:
evidence for mood incongruent recall. Journal of Personality and Social
Psychology 59 (August), 321–336.
Parsons, A.G., 2001. The association between daily weather and daily shopping
patterns. Australasian Marketing Journal 9 (2), 78–84.
Persinger, M.A., 1975. Lag responses in mood reports to changes in the weather
matrix. International Journal of Biometeorology 19 (2), 108–114.
Persinger, M.A., Levesque, B.F., 1983. Geophysical variables and behavior: XII: the
weather matrix accommodates large portions of variance of measured daily
mood. Perceptual and Motor Skills 57 (February), 868–870.
Prater, E., Biehl, M., Smith, A.M., 2001. International supply chain agility: tradeoffs
between ﬂexibility and uncertainty. International Journal of Operations and
Product Management 21 (5/6), 823–839.
Roslow, S., Li, T., Nicholls, J.A.F., 2000. Impact of situational variables and
demographic attributes in two seasons on purchase behavior. European
Journal of Marketing 34 (9/10), 1167–1180.
Sanders, J.L., Brizzolara, M.S., 1982. Relationships between weather and mood.
Journal of General Psychology 107 (July), 155–156.
Saunders, E.M., 1993. Stock prices and wall street weather. The American
Economic Review 83 (December), 1337–1345.
Schwarz, N., Clore, G.L., 1983. Mood, misattribution, and judgement of well-being:
informative and directive functions of affective states. Journal of Personality
and Social Psychology 45 (February), 513–523.
Sherman, E., Smith, R.B., 1987. Mood states of shoppers and store image:
promising interactions and possible behavioral effects. In: Wallendorf, M.,
Anderson, P. (Eds.), Advances in Consumer Research, vol. 14. Association for
Consumer Research, Provo, UT.
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520 519
Author's personal copy
Sobel, M.E., 1982. Asymptotic intervals for indirect effects in structural equations
models. In: Leinhart, S., San Francisco, C.A. (Eds.), Sociological Methodology,
vol. 13. Jossey-Bass, pp. 290–312.
Spies, K., Hesse, F., Loesch, K., 1997. Store atmosphere, mood, and purchasing
behavior. International Journal of Research in Marketing 14 (February),
Stain-Malmgrem, R., Kjellman, B.F., Aberg-Wistedt, A., 1998. Platelet serotonergic
functions and light therapy in seasonal affective disorder. Psychiatric Research
78 (May), 163–172.
Stecke, K.E., Kumar, S., 2009. Sources of supply chain disruptions, factors that
breed vulnerability, and mitigating strategies. Journal of Marketing Channels
16 (3), 193–226.
Steele, A.T., 1951. Weather’s effect on the sales of a department store. Journal of
Marketing 15 (April), 436–443.
Stoupel, E., Abramson, E., Sulkes, J., 1999. The effect of environmental physical
inﬂuences on suicide. How long is the delay? Archives of Suicide Research 5
Summers, Ta A., Hebert, P.R., 2001. Shedding some light on store atmospherics:
inﬂuence of illumination on consumer behavior. Journal of Business Research
54 (November), 145–150.
Thayer, R.E., Robert, N.J., McClain, T.M., 1994. Self-regulation of mood:
strategies for changing a bad mood, raising energy, and reducing
tension. Journal of Personality and Social Psychology 67 (November),
Trombley, M.A., 1997. Stock price and wall street weather: additional evidence.
Quarterly Journal of Business and Economics 36 (Summer), 11–21.
Underwood, B., Moore, B.S., Rosenhan, D.L., 1973. Affect and self-gratiﬁcation.
Developmental Psychology 8 (2), 209–214.
Watson, D., Anna, C.L., Tellegen, A., 1988. Development and validation of brief
measures of positive and negative affect: the PANAS scales. Journal of
Personality and Social Psychology 54 (June), 1063–1070.
Winer, R.S., 1999. Experimentation in the 21st century: the importance
of external validity. Journal of the Academy of Marketing Science 29 (3),
K.B. Murray et al. / Journal of Retailing and Consumer Services 17 (2010) 512–520520