Frank Oosterom
The indoor weather effects on consumer behavior
Master Thesis
Thesis Coach: Dr. Ed Peelen January 2015
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Preface
"Choose a topic you like" were the words that resounded in my head when I leaved the lecture of Thesis Proposal at the end of April 2014. I was a bit confused, because I expected the remark "choose a topic with a balance between managerial and academic relevance" or "choose a topic that is not adequately researched". During my journey home, I asked myself "what does really intrigues me? ". When I looked out of my car window I knew it. What really intrigues me for years is the weather. When I was a young boy, I checked the weather
forecasts several times a day, when snow was expected, or warm times were predicted. The next step was to find a research field that connects weather and marketing. After reading some academic articles I eventually chose this topic.
The search for adequate research questions and the creation of a conceptual model was difficult, but I eventually succeeded due to my supervisor dr. Ed Peelen. I would thank dr. Ed Peelen for his patience and invaluable support during the whole process of writing this thesis. In addition, I would also thank my fiancée Moniek de Bel for her patience and support and I want to apologise for all the evenings that you have spent alone while I was writing my thesis. In particular I want to thank my parents for making available of the experimental room and the reception of all the participants in their house.
The writing process of this thesis was a process of thinking, writing, re-writing and fine-tuning. The result is a master thesis that is complete but far from perfect, because of that, your feedback is most appreciated.
Enjoy reading,
Frank Oosterom
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Index
1.0 Abstract ... 4
2.0 Introduction ... 5
3.0 Theoretical background ... 8
3.1 Customer experience, mood, affect and emotions: ... 8
3.2 Weather influence ... 11
3.3 Recap weather influence ... 14
3.4 Indoor weather variables ... 15
3.5 Hypothesis ... 17 4.0 Method: ... 20 4.1 The experiment ... 20 4.2 Experiment groups ... 21 4.4 Questionnaire ... 21 4.5 Sample ... 22
4.6 Measurement and validity ... 23
4.7 Statically approach ... 23
5.0 Results and analyses ... 25
5.1 Reliability ... 25
5.2 Negative affect ... 26
5.3 Negative affect and shopping intention ... 28
5.4 Positive affect ... 31
5.5 Correlation ... 33
5.6 Purchase intentions ... 33
6.0 Discussion ... 36
6.1 Significant influence of ’’indoor’’ weather variables on negative affect ... 36
6.2 Direct relation between positive and negative affect ... 36
6.3 Affect influence on purchase intention ... 37
6.4 No influence of ’’indoor’’ weather variables on positive affect ... 38
6.5 Negative affect as a hygiene factor ... 38
6.6 No direct influence of shopping intention ... 39
6.7 Homogeneity of variance ... 40
6.8 Impact in model of customer experience ... 41
6.9 Limitations and directions for future research: ... 41
6.10 Managerial implications... 43 7.0 General conclusion... 45 Sources: ... 46 Appendix I: ... 54 Appendix II: ... 55 Appendix III: ... 56
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1.0 Abstract
Past research has shown that there is much uncertainty about weather, and how, weather conditions influence consumers. Almost no research has ever been done on the influence of indoor weather conditions on consumer behavior. We designed a conceptual model and examined seven hypotheses related to indoor weather conditions and consumer behavior. The results of our laboratory study give many new insights in customer experience. Significant evidence from an experiment with 60 participants, shows that ‘low’ indoor weather conditions have an impact on negative affect of consumers. This research also shows that positive affect is positively related with purchase intention and that negative affect is negatively related with purchase intention. The outcomes of the study provide support for four of the seven
hypotheses. Besides the support of the hypotheses, this research also shows that shopping intention is crucial in order to distinguish differences in negative affect under different weather conditions.
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2.0 Introduction
Customer experience is getting more and more important in daily retail environments. Retailers no longer create a competitive advantage by just selling excellent products with the best product attributes for the best price. Nowadays retailers create a competitive advantage by delivering a total customer experience better than competitors (Verhoef , Lemon,
Parasuraman, Roggeveen, Tsiros and Schlessinger, 2009). According to Meyer and Schwager (2007) customer experience is about the totality of all interactions of a customer with a company.
Examples of creating customer experience are the stores of Perry Sport. Perry Sport is a retailer in outdoor sports articles and the interior of the stores radiates outdoor sports. There are even climbing walls in the stores. Other examples of companies that give customer experience a central place in their business operations are Apple (Gallo, 2012) and
BMW(Meyer and Schwager, 2007). Apple even creates online communities where people can find and share solutions with Apple users around the world. For BMW, customer experience is very important because there are many competitors in that market segment. An example of how BMW tries to create customer experience is the active sound design in the BMW M5. The car builders link the stereo system with the engine, in order to simulate the noise and experience of a twin-turbocharged V8 engine. BMW even creates a program named “dream it, build it, drive it’’ where people can design their own car, in order to create a personalized customer experience (Barsch, 2011).
So noting that customer experience is key in gaining competitive advantage and indispensable for future business success, we have to ascertain all aspects that influence customer
experience.
The idea that customer experience is about the total experience is consistent with findings of Kotler (1973). Kotler stated that the shopping environment is getting more and more
important, because nowadays it is not just about the product attributes, but consumers care about the total product. Atmosphere is sometimes even more important than the product itself (Kotler,1973). This concept is supported by the research of Sezgin and Küçükköylü (2014). These researchers showed that there is a significant effect of store atmosphere on the store’s image.
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Interesting to know is how a company can influence the customer experience in order to create competitive advantage. Much literature is written about this subject and Verhoef et al., investigated the existing literature on customer experience and came to a conceptual model.
The conceptual model of customer experience creation described the following variables: social environment, service interface, retail atmosphere, assortment, price, retail brand and customer experiences in alternative channels. These variables are the building blocks of his conceptual model.
Three variables need further research. These are social environment, service interface and retail brand. The other four variables are considered as adequately researched (Verhoef et al., 2009). These are retail atmosphere, assortment, price and customer experiences in alternative channels.
Verhoef et al. suggest that an adequate amount of research has been done on variables of retail atmosphere. In the model, they defined retail atmosphere as design, scents, temperature and music. Verhoef and his fellow researchers mention three articles about retail atmosphere, but according to Turley and Milliman (2000), retail atmosphere is more than design, scents music and temperature. Five categories of atmospheric variables are distinguishable: external
variables, general interior variables, layout and design variables, point-of-purchase and decoration variables and human variables. By general interior variables we can think about music, colors, lighting, odor, temperature and cleanness. According to Turley and Milliman, many research is done on the influence of music, but less research has been done on impact of odor.
The results of the research of Verhoef et al. (2009) showed that retail atmosphere had an effect on emotional states or affect. The impact of retail atmosphere on affect was measured by the variable excitement. According to Watson et al. (1988), excitement is just a small part of affect. Excitement is just one of the four indicators for high positive affect. Besides the inadequate manner of measuring affect, none of the studies that are mentioned by Verhoef et al. (2009) actually looked for the impact of temperature and other "indoor" weather variables.
Beside previous research mentioned in the article of Verhoef et al., much other research is done on different aspects of retail atmosphere. Think about influence of music on consumer reactions on waiting time (Hui, Dube and Chebat, 1995), or the research that showed that colors influence stimulated purchase and time spend in a shop (Bellizi and Hite, 1992).
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We can state that retail atmosphere or physical environment may influence consumer’s emotional states (Bloch, Ridgway and Dawson, 1994; Jacobs, 1984; Kowinsld, 1985), and there is a link between store environment, the affective stages of pleasure, arousal and behavior intentions (Baker, Grewal and Levy, 1992).
However, the conclusion that retail atmosphere has been adequately researched (Verhoef et al., 2009) is not accepted by us. Especially the impact of "indoor" weather variables as a part of retail atmosphere needs further research .
Research has been done on the influence of "outdoor" weather on consumer behavior (Linden, 1962; Murray, Munro, Finn and Leszczyc, 2010) and on the impact of weather on mood (Denissen, Penke, Butalid and Aken, 2008; Keller, Fredrickson, Ybarra, Cote, Johnson and Mikels, 2005; Watson, 2000; Sanders and Brizollara,1982; Goldstein,1972;
Cunningham,1979). But the conclusions are contradicted and it is unclear if we can apply the outcomes of outdoor weather to indoor environments.
Because the lack of research on the effects of "indoor" weather variables on affect, we want
to investigate the influence of temperature, air pressure and light on affect, and how affect influences consumer behavior.
The academic relevance of this paper contributes to a better understanding of the retail atmosphere part of the model of customer experience creation (Verhoef et al.,2009). This research also contributes in particular to the understanding of indoor weather as a sub-part of retail atmosphere.
The managerial relevance of this paper is in that findings of this study can be directly
implement in the retail environments, in order to influence consumer experience and behavior. It is important to investigate the effects of indoor weather variables on affect, because the contradictions in outcomes of research on outdoor weather variables makes it unclear which and how we can change weather variables in order to create increase in customer experience.
The paper starts with the theoretical elaboration of the different aspects of indoor weather and a framework to distinguish the different hypothesis. To test the hypothesis we conduct a laboratorial study. In this laboratorial study, participants are exposed to the different indoor weather variables and they have to shop in an online environment.
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3.0 Theoretical background
This chapter gives an overview of the existing relevant literature on customer experience, mood and weather influence. A good understanding of these theoretical fields are necessary in order to investigate the indoor weather effects. We will start with a discussion about the definitions of customer experience and mood, because there are many different definitions in the existing literature.
3.1 Customer experience, mood, affect and emotions:
As mentioned, customer experience is getting more and more important in order to gain a competitive advantage. Verhoef et al. (2009) showed in his model of customer experience creation that temperature as one of the weather variables, had indeed influence on customer experience. But what is actually the definition of customer experience?
Literature is not very clear on this point and there are different definitions. Gentile, Spiller and Noci(2007) stated that ’’The customer experience originates from a set of interactions between customers, sellers, company, product or part of its organization, which provoke a reaction. This experience is strictly personal and implies the customer’s involvement at different levels (rational, emotional, sensorial, physical and spiritual).’’
Another definition is that “Customer experience is the internal and subjective response customers have to any direct or indirect contact with a company. Direct contact generally occurs in the course of purchase, use, and service and is usually initiated by the customer. Indirect contact most often involves unplanned encounters with representatives of a
company’s products, service or brands and takes the form of word-of-mouth recommendations or criticisms, advertising, news reports, reviews and so forth.” (Meyer and Schwager, 2007). In the definition of Meyer and Schwager, the customer experience arises mainly as a response on interaction between a company and the consumer. According to Gentile et al. (2007) it is not only the company that interacts with the consumer, but customer experience is also created by the interaction between consumers. However response and interaction between consumer and company are central in both definitions.
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Verhoef et al. (2009) suggest that customer experience involves the customer’s cognitive, affective, emotional, social and physical responses to the retailer. Customer experience is about the total experience. So the customer experience is eventually a package of mental (emotional and cognitive) and physical responses on the whole experience with the product or company. Important to note is that affect and emotion play an important role in creating customer experience.
If affect and emotion play an important role in creating customer experience. We have to define what we mean by mood, affect and emotion.
Many researchers used the term mood interchangeably with affect and emotion(e.g. Solomon, 1980; Bower, 1981; Watson and Tellegen, 1985). But according to Piët (2010) mood is a prolonged temper background of experiences that interpret endured emotional stimuli filtered and colors. Tomkins (1962) found that affects are observable expressions and emotions are considered to be awareness of affects. According to the psychological dictionary, affects are observable expressions of emotions. So we can assume that the terms affect, emotion and mood are closely related.
Affect is a term that covers a range of feelings that people experience. Affect is more like an umbrella that encompasses mood and emotion. Emotions are intense feelings that are directed to someone or something (Frijda, 1993). Moods are less intense than emotions and mood is more an overall metal state (Weiss and Cropanzano, 1996). So emotions are more caused by a specific event. For example, getting a present for your birthday creates a positive emotion. But at the same time you can be in a depressed mood because you realize that life is going too fast.
Figure 3 shows a schematic representation of the relation between affect, mood and emotion.
Figure 3. Source: Hume, D.: Emotions and Moods. In Robbins, S. P., Judge, T. A.(Eds.), Organizational Behavior, pp. 258-297.
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In literature, there is a distinction made between different types of affect. Than the question occurs, what is the exact difference between positive affect and negative affect?
In the book of Organizational Behavior, D. Hume defined positive affect as: ’’A mood dimension consisting of specific positive emotions like excitement, self-assurance, and cheerfulness at the high end, and boredom, sluggishness, and tiredness at the low end.’’ Negative affect is defined as: ’’A mood dimension consisting of nervousness, stress, and anxiety at the high end, and relaxation, tranquility, and poise at the low end’’.
If you hear about positive and negative affect, you might think that these two mood factors are opposites. These terms are in fact distinctive dimensions (Watson et al. 1988).
Positive affect (PA) reflects the extent to which a person feels enthusiastic, active and alert. High positive affect is a state of high energy, full concentration and pleasurable engagement. Whereas low positive affect is characterized by sadness and lethargy. Negative affect (NA) is a general dimension of subjective distress and unpleasurable engagement that is a variety of aversive mood states, including anger, contempt, disgust, guilt, fear and nervousness. Low negative affect being stated as calmness and serenity (Watson et al. 1988).
In figure 4 you can find which emotions are related to which kind of affects.
Figure 4. Hume, D.: Emotions and Moods. In Robbins, S. P., Judge, T. A.(Eds.), Organizational Behavior, pp. 258-297.
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According to Watson (2000) we can use positive and negative affect to measure a mood state of people. Watson find that people are in their best moods at the end of a week (high positive affect and low negative affect) and that people and in their worst mood at the beginning of a week (highest negative affect and lowest positive affect)
In this research we suppose that mood is more an overall mental state (Frijda, 1993). And that this overall state is determined by the ratio between positive affect and negative affect (Watson et al., 1988).
Positive mood stimulates mood maintenance (Carlson, Charlin and Miller 1988) and people in a good mood are more likely to reward themselves and spend more (Murray et al, 2010). The assumption that a positive mood state has a positive effect on consumer behavior is according to the customer experience model of Verhoef et al. (2009). So for this research we suppose that there is empirical evidence that a positive mood state has a positive effect on consumer behavior.
3.2 Weather influence
"Revised US GDP figures show shrinking economy over harsh winter". This is the headline of The Guardian on May 29, 2014. The cold winter in North America has led to a decline of the world largest economy of 1% during the first quarter of 2014. This is just one of the examples that shows that weather can have a major impact on consumers and their purchase behavior. Think about the decrease in ice cream sold during a cold summer. This loss of seasonal sales can be permanently lost (Linden, 1962).
Weather effects are more than just purchases decline. Research showed that weather impacts the stock market (Saunders, 1993) and that it is actually the sunshine that is significantly related with stock returns (Hirshleifer and Lyler, 2003). This is according to the results that showed that as exposure to sunlight increases, negative affect decreases and consumer spending tends to increase (Murray et al., 2010).
If we suppose that there is a relation between mood and consumer behavior, it would be interesting to define the relation between weather and mood. As mentioned, much research is done on this subject but results are contradictory. We summarize the most important research on this topic.
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Denissen, Penke, Butalid and Aken (2008) showed that pleasant weather did not influence people’s positive mood in general. This outcome is conflicting with research that showed that besides sunlight and high air pressure, temperature and even low levels of humidity have an influence on mood (Sanders brizollara,1982; Goldstein,1972; Cunningham,1979).
Grohol (2008) suggests that Denissen et al. (2008) indeed showed that there is a very small correlation between weather and mood, but that there is way more research showing that weather indeed influence people’s mood.
Much of this research is done with small samples that are not representative, but that is not the case for Faust, Weidmann and Wehmer (1974). They used 16000 students for their research and they found that 23% of all respondents consider themselves as sensitive to weather variables. 18% of the boys and 29% of all girls. The most examples of common symptoms were tiredness, dislike of work, lack of concentration and sensitiveness. More than half of the people that consider themselves as sensitive to weather variables, also consider themselves as pre-sensitive to weather changes.
More proof of a relation between weather and mood are the outcomes of the violence research of Rotton and Cohn (2000). In this research, they showed that assault was a linear function of temperature during night. But there was no significant correlation between assault and
temperature on the warmest hours of the day. According to the Negative Affect Escape (NAE) model, the relations between environmental conditions (e.g. temperature) are mediated by negative affects (Baron and Bell, 1976). They found that aggression was an inverted u shaped function of the negative affect that is reported by the participants of the research.
The outcomes of the research of Rotten and Cohen are in accordance with the results of the research of Hsiang et al. (2013). Hsiang et al. found that intergroup conflicts increased by 14% and interpersonal conflicts with 4%, when temperature rises. Rising temperatures did not only increase intergroup conflicts, but also decreased life satisfaction.
Connolly (2013) found that women experienced significantly more life satisfaction on days with low temperatures and no rain, than on days with high temperatures and rain. This
contradicts with the idea that high temperature is associated with positive mood (Cunningham and Hoffman,1984).
Grohol (2008) even suggests that many research shows that weather impacts our moods, and that it sometimes even leads to major problems. People suffer from seasonal affective disorder
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(SAD). Seasonal affective disorder is a form of current depressive disorder. The problem could be caused by a lack of serotonin. The best treatment is exposure towards bright (10.000 lux) artificial light in the morning (Partonen and Lönnqvist, 1998).
The existence of seasonal affective disorder appears to be strong evidence for an existing relation between weather factors and mood. Partonen en Lönnqvist (1998) also provide some evidence that artificial light is important in shaping people’s mood. If we determine that artificial light is important in mood shaping, we can conclude that artificial light is a very important component of indoor weather.
The different outcomes in literature about the impact of weather on mood are maybe
explained by the different impact on different personalities. Klimstra et al. (2011), found that 17% of the respondents are summer lovers. This means that people are more happy and less angry on days with higher temperatures and sunshine. 27% are summer haters. This means that they are less happy and more angry on days with summer weather. 9% of the respondents are considered as rain haters. This means that they are less happy and more angry on rainy days. Important is the group of 47% that are unaffected by weather. If these outcomes are representative for the global population, it supports the idea that indoor weather variables can be used to influence consumer purchase intentions.
Persinger and Levesque (1983) examined the effects of temperature, humidity, wind speed, sunshine, barometric pressure, geomagnetic activity and precipitation on mood. They found that in 40% of the evaluations mood changes were caused by meteorological effects. Barometric pressure and sunshine have the greatest impact on the outcomes of the mood scale.
Remarkable are the results of the studies of Barker et al. (1994) and Stoupel et al. (1999). They found that the number of suicides rise when air pressure increase and wind decrease. It is even possible to find a seasonal pattern in committed suicides. There is an increase in suicides during the spring and early summer, and there is a decrease during autumn and winter (Christodoulou, Douzenis, Papadopoulos, Papadopoulou, Bouras, Gournellis, Lykouras, 2012). A similar kind of research showed that outdoor workers are more likely to commit suicide in the spring and that people who work inside more often commit suicide during the summer months (Koskinen, Pukkila, Hakko, Tiihonen, Väisänen, Särkioja, Räsänen, 2002).
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High air pressure(Goldstein,1972), high temperatures (Cunningham and Hoffman,1984) and bright light (Cunningham,1979) where associated with positive mood.
Murray et al. (2010) found that higher levels of sunlight lead to a reduction of negative affect. The results of the research of Murray et al. are contradicted with the research of neurologist dr. A. Pijpers (2014), who writes that by an absence of light, the suprachiasmatic nucleus gives the pineal a sign to create the hormone melatonin. This hormone creates sleepiness. The suprachiasmatic nucleus also provides a decrease in temperature and blood pressure. When light is entering your eyes, the body stops making melatonin. So when you get sleepy and your blood pressure and body temperature decrease, it is plausible that people score lower on jittery, nervosity, irritability and hostility.
It is even possible that the reduction of negative affect had a greater impact on people’s mood than the increase of positive affect. This is according to the idea that joy is not a mood or affect, but a natural state of being that occurs by the absence of negative affect (LeDoux, J.E., 1996).
3.3 Recap weather influence
We can ascertain that the literature is not unanimous about the influence of weather on mood. Some research showed that there is no influence, other research showed that there is actually a relation. Factors like gender or job seems to have an influence on the manner in which
weather variables can influence mood.
We discovered that we can distinguish two levels of weather influence in the literature. We can distinguish influence on macro and micro level. By the weather influence on macro level, we mean the influence of weather on peoples total mental state. Think for instance of the influence of weather (seasons) on suicide or depressions.
By the weather influence on micro level, we mean the influence of weather on positive or negative affect, in order to create mood. Think for instance of the influence of temperature. Some research showed that temperature is positively related toward positive mood and other research showed that high temperatures are related toward a decrease in life satisfaction.
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With our research, we want to investigate the influence on the micro level. This means that we will look for influence of weather variables, that are present in a retail environment , on people’s affect. We do this in order to investigate the weather as part of the retail atmosphere.
The existing literature shows evidence to assume that at least five weather variables influence people’s affect. These variables are: light, air pressure, humidity, rain and temperature. This research will focus on three of the five weather variables. These three variables are light temperature and air pressure. We chose these three variables because they are always present in an indoor environment, they can be measured and they often occur together. It would be interesting to know how people respond toward different stages of the indoor weather variables.
Before we can investigate the impact of the different indoor weather variables, it is important to clarify what we mean by the different stages in temperature, air pressure and light.
3.4 Indoor weather variables
In this research we use the term high temperature. By high we mean a temperature of 25 degrees Celsius. Research shows that the productivity of people decreases about 2% for every single degree above 25 degrees Celsius (Seppänen, Fisk and Faulkner, 2003).
Other research shows that people perform best at 27 degrees Celsius, (Pelper 1968). But Josean Perez, Julio Montano and Jose Perez show that students have significant better test results when they are in a classroom with a temperature of 23 degrees Celsius, than in a classroom with a temperature of 27 degrees Celsius. But the reverse is also true. Students perform worse when the temperature is 18 degrees Celsius (see figure 4.0)
The conclusion that people underperform in colder environments are according to the findings of Seo, Kim, Ryan, Gunstad, Glickman and Muller (2013). They found that people with lower body temperatures (35/36 degrees Celsius) perform significantly worse on a Stroop Effect Test (reading and pronouncing the written color that is printed on paper of a different color), than people with a normal body temperature.
16 Figure 4.0: Room temperature and its impact on student test scores. (Perez, J. Montano, J. and Perez, J. )
So in this research we consider two ranges of temperature. With high temperature we mean a temperature of 25 degrees. Low temperature is 17 or 18 degrees.
The second variable in indoor weather is air pressure. High air pressure in combination with high temperature has a positive influence on people’s mood (Keller et al. 2005). But what is actually air pressure?
According to the website of USA Today, air pressure is force exerted by the weight of the air molecules. Air molecules take up space but can be compressed, because there is a empty space around them.
According to the website of luchtdruk.com (2012) In The Netherlands, at sea level, we consider that air pressure is high when it is above 1015hPa (Hectopascal). 1015 hPa is the middle line and an air pressure below 1015hPa is considered as low air pressure. For this research we definelow air pressure as a pressure below 1013 hPa and high air pressure as a pressure above 1017 hPa.
The third and final variable of indoor weather is light. We have opted for artificial light because artificial light is under human control. In this research we used two stages of light. For the bright light conditions, we used a lamp of 600 watt and in low light conditions we used a twilight lamp of 20 watt.
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In this research participants are tested under two weather conditions. These are high and low weather conditions. Each of the weather conditions includes three indoor weather variables: temperature, air pressure and light.
In this research we mean by high weather conditions, temperatures of 25 degrees, air pressure above 1017 hPa and an intensity of light of 600 watt. With low weather conditions we mean, temperatures of 17 degrees, air pressure below 1013 hPa and an intensity of light of 20 watt.
The described theory about customer experience, mood and weather influences leads to the following conceptual model:
With this conceptual framework we will provide insights in the relation between indoor weather variables and mood. We will also show the influence of shopping intention on mood (ratio of negative and positive affect). Finally, we will provide evidence for the relation between affect and purchase intention.
3.5 Hypothesis
In order to investigate how weather influence customer’s mood, we present seven hypotheses. These hypotheses are based on literature and our conceptual model.
As previously mentioned, high temperature is associated with positive mood (Howard and Hoffman,1984). Besides high temperature, sunlight, high air pressure, temperature and low levels of humidity also influence mood (Sanders and Brizollara,1982; Goldstein,1972; Cunningham,1979).
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Murray et al. (2010) found that higher levels of sunlight lead to a reduction of negative affectand Persinger and Levesque (1983) found that barometric pressure and sunshine have the greatest impact on the outcomes of the mood scale.
If we connect this research with the Panas mood scale of Watson et al. (1988) this leads to the following two hypotheses:
H1: High indoor weather conditions, have a positive relationship with positive affect.
H2: Low indoor weather conditions, have a negative relationship with negative affect.
We have chosen for a combination of the indoor weather variables because we have to deal with research scope limitations. We chose for this combination of weather variables because literature shows that each of the variables on itself has an influence on mood, and in nature these combinations of variables often appear together.
Watson (2000) showed that people are in their best moods at the end of a week. This means that positive affect is high and negative affect is low. This could give us the assumption that positive and negative affect are opposites of each other, and that there is a direct relation between positive affect and negative affect. But according to Watson et al. (1988) positive and negative affect are in fact distinctive dimensions. This gives us the assumption that there should be no direct relation between positive affect and negative affect. This assumption leads to the following hypothesis:
H3: There is no negative relation between positive affect and negative affect.
It is important to notice that shopping variables or arousal have a different impact on different types of consumers (Kaltcheva & Barton, 2006). Kaltcheva and Weitz also found that
recreational shopper’s pleasantness feeling increases from high arousal. Conversely it appears that high arousal environments decrease pleasantness of task oriented shoppers. So the
deployment of high or low arousal environment depends on the type of consumer. Given this finding, we suppose the following hypotheses:
H4: Recreational shopping intentions are positively related with positive affect and negatively related with negative affect under high weather conditions.
H5: Task-oriented shopping intentions are positively related with positive affect and negatively related with negative affect under low weather conditions
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According to Park, Lennon and Stoel (2005), there is a relation between positive affect and low negative affect and purchase intention.
We want to measure purchase intention, because arising purchase intention is the key to predict actual purchase (Keller,2001). Other studies showed that consumer’s final decision to buy a product depends on their intention (Ghosh,1990). So according to Gosh, purchase intention is a good predictor of actual purchase. The assumption that high weather variables are indirectly (by mood) related toward purchase intentions are supported by the research of Areni and Kim (1994). These researchers looked for the impact of lightning on consumer behavior, and they found that people under bright light conditions buy significantly more than consumers under soft light conditions. This is according to the results that showed that as exposure to sunlight increases, negative affect decreases and consumer spending tends to increase (Murray et al., 2010).
Given this findings we suppose the following hypotheses:
H6: There is a positive relationship between positive affect and purchase intention.
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4.0 Method:
With this study we want to investigate the "indoor weather" effects on affect, and we want to look for the relation between affect and purchase intention. To investigate this, we have chosen for a lab experiment because the dependent variable (affect) starts in our subconscious and it is almost impossible for respondents to imagine how they will respond on variables that you are normally not aware of. So with a lab experiment you can measure the real response on the independent variables.
This method chapter starts with a description of the lab experiment. After this we explain the questionnaire that we used, and then we discuss the sample size, validity and reliability of the research. In the last part of this method section, a brief description is given about the
statistical methods of test construction.
4.1 The experiment
The independent variables are temperature, light and air pressure. The intensity of these variables will be different in each experiment. Affect is both a dependent and an independent variable. It is independent in relation to"indoor weather" but it is a dependent variable in relation to purchase intention.
In this study we used an experiment. The experiment was constructed as follows:
The participant have to sit on a chair in the conditioned environment for five minutes. We do this in order to let the participants acclimatize. After five minutes, the participants receive a briefing. Half of all participants have to imagine that they have to organize a dinner for 25 people and that they may choose one menu on the website: www.ideaalbuffet.nl. They get five minutes to consider the nine different menus and after this five minutes, we asked the
participants which menu they would like to choose (task-oriented shoppers).
The other half of the group did not have an explicit task. We told them that they were exploring options for a birthday dinner and we just asked them to visit the same website for five minutes (recreational shoppers).
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After these five minutes, participants (both recreational and task-oriented) were asked to fill out a questionnaire to measure the impact of the independent variables on affect and purchase intention.
The participants have to do this experiment in a laboratory environment where we can manipulate the light, temperature and air pressure.
The laboratory was set up in a study room. In this laboratory, we mounted a heater to influence the temperature, we darkened the windows and installed a lamp with a dimming button. The laboratory is located in a quiet neighborhood in order to avoid noise from outside. The laboratory environment reflects the natural environment where people surf the internet. However, we removed the paintings from the wall and covered the bookcase with a blanket, in order to avoid as much unintentional arousal as possible. Pictures of the laboratory setting can be found in appendix I.
4.2 Experiment groups
In the first experiment group (EG 1), participants were exposed to high temperature, bright light and high air pressure. In this group we made a distinction between task oriented
shoppers (EG 1T) and recreational shoppers (EG 1R). Fifteen participants were classified as EG 1T and fifteen participants were classified as group EG 1R
In the second experiment group (EG 2), participants were exposed to low temperatures, darkness and, and low air pressures. In this group we made a distinction between task oriented shoppers (EG 2T) and recreational shoppers (EG 2R). Fifteen participants were classified as EG 2T and fifteen participants were classified as group EG 2R
A total of four groups where involved in the experiment.
4.4 Questionnaire
As previously mentioned, participants received a questionnaire after browsing the internet for five minutes that they have surfed on the website. With this questionnaire we want to measure the influence of the independent variables on affect and purchase intention.
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To measure the influence on affect we choose the Positive Affect and Negative Affect Schedule. We chose this scale because the 10-item mood scale is highly reliable and valid (Watson, D., Clark, L. A. & Tellegen, A. 1988). According to Watson, Clark and Tellegen, the 10-item scale is internally consistent and efficient. Watson et.al tested the PANAS scale and found a Cronbach’s coefficient ranging from 0.86 to 0.90 for positive affect, and a Cronbach’s coefficient ranging from 0.84 to 0.87 for negative affect (Watson et.al, 1988).
To measure the effect of positive affect and negative affect on purchase intention we also used a questionnaire. To ensure validity and reliability, a 7-item measure scale was chosen that was developed made by Dodds, Monroe and Grewal(1991) and a questionnaire that is used by Kao-Chun (2008).
All questions used in the questionnaire were derived from other studies that were conducted in the English language. Because the participants in this study were all Dutch, the
questionnaire has to be translated. As a quality control measure, the questions were translated by two people, independently. Several minor differences were found, discussed and a
consensus was reached
As mentioned, the questionnaire was divided into two parts. In the first part we measure the purchase intention on a 7 item measure scale and in the second part of the questionnaire people have to fill out the PANAS scale (see fig. 2.)
4.5 Sample
In this study, we conduct an experiment to condition the participants to measure the impact of "indoor weather" on affect and purchase intention. But how many participants are needed to maintain adequate power in order to detect differences and when are the outcomes reliable? Harris (1985) suggests that the number of participants should exceed the number of predictor variables + 50. In our construct, we can distinguish three predictor variables (weather
conditions, shopping intentions and purchase intentions). So according to Harris (1985) we need at least 53 participants. As the experiment involves four groups, we decided to invite 60 people in order to have four groups of 15 people.
To select the participant for the experiment we chose the convenience sampling strategy. This is of course the easiest way to gather participants for the research, but it will not impact the
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validity of the outcomes. This is because people’s demographic characteristics do not
influence the outcomes on the affect scale. Gender for instance did not influence the outcomes on the affect scale (Watson et.al, 1988). More suggestible are the outcomes of the purchase intention questionnaire. But the group of participants is so diverse in gender age and income that it is a reflection of the average consumer.
4.6 Measurement and validity
To test reliability you have to measure the same outcomes if you use the same test for a new sample of the existing population. In statistics reliability is indicated with the Cronbach alfa. Reliability is also defined as "the extent that measurements are repeatable" (Nunnally and Bernstein, 1994)
It is generally accepted that the Cronbach alpha should be at least 0.7 Bryman (2004) stated that the Cronbach alpha should be in between 0.7 and 0.98 (high reliability), but Cronbach alpha below 0.60 should be rejected (Blaikie 2006).
4.7 Statistical approach
To measure the differences in affect and between the groups, we used a one-way ANOVA. We choose the one-way ANOVA because the construct has multiple independent variables
We start with a test to look for different outcomes in negative affect and positive affect between the different groups. When we find a significant P value(< 0,05), we can consider that it is unlikely that the outcomes are based on coincidence, and that the means of negative affect and positive affect are different in the different groups.
If we see that the weather variables influence peoples affect, it is important to find which outcomes significantly differ from each other. Therefore we used a Post Hoc Multiple Comparison test. If the P value is low (<0.05) we can assume that there is a significantly difference in outcome between the independent variables.
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Thereafter we looked for the relation between affect and purchase intention. To test this we used regression analysis. We chose the regression analysis because we used total positive affect and total negative affect as two numerical discrete predictor variables. The variables are discrete because the scores of positive and negative affect can only be counted as whole numbers on the scale (Field, 2009). The dependent variable is purchase intention.
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5.0 Results and analyses
5.1 Reliability
In order to test the reliability of the used scale we have measured the Cronbach alfa for both negative and positive affect.
Results in figure 5.0 showed that the Cronbach alpha is 0.87 for negative affect and 0.80 for positive affect. According to Bryman (2004) both scales are reliable. Watson et.al test the PANAS scale and found a Cronbach alfa ranging from 0.86 to 0.90 for positive affect, and a Cronbach alfa ranging from 0.84 to 0.87 for negative affect (Watson et.al, 1988). Our results are almost similar to the results of Watson et al. (1988)
Crombach’s alfa for purchase intention is 0.88. The questions used to measure purchase intention come from the research of Dodds et.al and Kao-Chun (2008). Cronbach’s alpha for the research of Dodds et.al is 0.96. The Cronbach’s alpha for the research of Kao-Chun (2008) is 0.74. So according to Bryman (2004) our measurement of purchase intention is reliable.
Figure 5.0 Cronbach’s alfa of negative affect
Figure 5.1 Cronbach’s alfa of positive affect
Figure 5.2 Cronbach’s alfa of purchase intention
We looked for possible optimization of the Cronbach alfa, but results shown in figure 5.3(Appendix II) show that optimization is almost impossible, so we did not exclude any of the questions asked. A full correlation matrix is also included in Appendix II.
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We first showed the results of tests that are related with the hypothesis about negative affect. Afterwards we showed the results of the tests that are related with the hypothesis about positive affect.
5.2 Negative affect
To test if there is a significant impact of weather variables on negative affect without controlling for shopping intention, we drove a one-way ANOVA.
Field (2009) said that the assumption of homogeneity of variances means that as you go through levels of one variable, the levels of other variables should not change. To test the homogeneity of variance, we use the Levine’s test. (Field, 2009).
The Levine’s test is significant when we compare the means of the total negative affect
(p=0.001). This means that Ho is rejected and that there is a significant difference between the variance in the different groups. According to Field (2009) we can solve this problem in several ways.
1 We can transform the data to get homogeneous variances.
The best transformation results came from a reciprocal transformation, but the results were still significant.
2 We can calculate the Welch F and the Brown-Forsythe F.
Important to know is that we must interpret the outcomes in the same way as the Levene's test.
As can be seen in figure 6.0, we calculated the Robust Tests of Equality of Means, and after the removal of some outliers, the value of the Welch and Brown-Forsythe test is 0.053. Which presupposes that means of different groups are not equal.
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Figure 6.0 Test of Homogeneity of Variance, Robust test of Equality of Means and ANOVA.
The ANOVA in figure 6.0 showed that there is a significant effect on perceived negative affect, F(1,55)= 4.090, p< 0.048. The ANOVA also showed a SST of 658.98. The SSM is 45.61 and the SSR is 613.37. The F-ration= 4.09 This means that the variance explained by the model is four times more than the variance explained by other influences. So the
participants exposed to low weather conditions, score significantly higher on negative affect that participants exposed to high weather conditions.
These results give support to the following hypothesis:
H2: Low indoor weather conditions, have a negative relationship with negative affect.
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5.3 Negative affect and shopping intention
To test the influence of shopping intention on negative affect, we drove a one-way ANOVA. As shown in figure 7.0 the test of the homogeneity of variances is >0.05, which means that there is a significant difference between the variance in the different groups
Figure 7.0 Levine’s test of homogeneity of variances of negative affect
In order to solve the difference in variance, we calculate the Robust Tests of Equality of Means. Figure 7.5 showed a Welch value of 0.075. So the means of the different groups are not equal.
Figure 7.1 Robust test of equality of means of negative affect
The result of the ANOVA in figure 7.2 showed a SST of 960.85. The SSM is 134.98 and the SSR is 825.87. The F-ratio gives information about the explained variance in comparison with the unexplained variance. So a high F-ratio implies a high explained versus unexplained variance.
Figure 7.2 ANOVA negative affect
The F-ratio is 3.05 which means that the difference in variance, that is explained by different groups is almost three times more that the variance that can be explained by difference within
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the groups (Sig. 0.036). We conclude that H0 is rejected and that there is one mean of negative affect that is significant different.
For a comparison of the difference between the groups we calculate the Post Hoc Multiple comparisons. Results of the Post Hoc Test and the descriptive are showed in figure 7.3.
Figure 7.3 Descriptives and post hoc multiple comparisons of negative affect
There is a significantly effect of indoor weather variables and shopping orientation (task or recreational) on perceived negative affect, F(3,56) = 3.05, p< 0.05. The Turkey post-hoc test (fig 7.3) revealed that the perceived negative affect was significantly higher in the group in low indoor weather conditions and recreational shopping intentions compared to the group in high indoor weather conditions and task oriented shopping intentions (p=0.034). There was no significant difference of the group in high weather conditions and a recreational shopping orientation with other groups. There is also no significant difference of the groups in low weather conditions and task oriented shopping intentions with other groups.
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Figure 7.4 Graphical representation of negative affect of different groups
According to the results we see an increasing score in negative affect with significant difference between the mean of group 1 and 4. So the decreasing indoor weather variables have a significant increasing effect on negative affect. This increasing effect is significant for the group in low indoor weather conditions and recreational shopping intentions compared to the group in high indoor weather conditions and task oriented shopping intentions (p=0.034).
These outcomes did not support the assumption that neither recreational or task-oriented shopping intention have a significant influence on negative affect. This implies the partial rejection of H4 and H5.
H4: Recreational shopping intention are positively related with positive affect and negatively related with negative affect under high weather conditions.
H5: Task-oriented shopping intentions are positively related with positive affect and negatively related with negative affect under low weather conditions
31 5.4 Positive affect
The test of the Homogeneity of Variances (fig 8.0) is not significant, which means that there is homogeneity of variance.
Figure 8.0 Levene's test of homogeneity of variances of positive affect
According to the ANOVA shown in figure 8.1, there is no support for the idea that some of the difference in variance is significantly caused by the model (p=0.75). F(3,56)=0.305. So most of the difference in variance is caused by differences within the groups, and a minor (non-significant) difference in variance is caused by the influence of the weather variables on positive affect.
Figure 8.1 ANOVA positive affect
The Turkey post-hoc test shown in figure 8.1, determined that the mean of the different
groups is nearly the same for all groups, and we cannot find significant difference between the groups. What is striking is that SD is higher in groups with the low weather conditions for both recreational shoppers and task oriented shoppers (SD both groups=1.60).
32 Figure 8.2 Descriptives and post hoc multiple comparisons of positive affect
The results of the tested influence of weather variables on purchase intention did not support hypothesis 1.
H1: High indoor weather conditions, have a positive relationship with positive affect.
Besides the fact that these results rejects H1, they also fail to support the influence of
shopping intention on positive mood mentioned in hypothesis four and five. These outcomes, together with the previous outcomes on negative affect gives us no support for hypotheses four and five
H4: Recreational shopping intention are positively related with positive affect and negatively related with negative affect under high weather conditions.
H5: Task-oriented shopping intentions are positively related with positive affect and negatively related with negative affect under low weather conditions
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5.5 Correlation
In order to find a possible correlation between positive and negative affect, we compare total positive affect and total negative affect. The descriptive statistics and correlations are shown in figure 9.0
Figure 9.0: Descriptive statistics and correlations of TPA and TNA
The results show that there is a negative correlation (-0.09) between total positive affect and total negative affect. However p= 0.51, which means that the negative correlation is not significant.
The results supports hypothesis 3
H3: There is no negative relation between positive affect and negative affect.
5.6 Purchase intentions
In order to find the relationship between mood (positive and negative affect) and purchase intention, we ran a regression analyses. This is possible because we used two numerical discrete predictor variables (total negative affect and total positive affect) and one quantitative outcome variable (purchase intention). Figure 10.0 shows the model summary.
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R has a value of 0.60. This is the simple correlation between affect and purchase intention. The value of R Square tells us that affect causes 37% of the variation in purchase intention. So we now know that purchase intention is caused for 63% by other factors.
The ANOVA showed in figure 10.1 tells us that the F-value is 16.40. A high F-value means that a lot of variance is explained by the model, and this particular F-value means that the variation that is explained by the model is more than 16 times bigger that the variation that is explained by other factors. And more importantly this value is (<0.05) significant, which means that we can reject the H0 (there is no difference caused by the model).
Figure 10.1 ANOVA
The B value shown in figure 10.2 is 3.21. This is the mean of purchase intention when there is no TPA of TNA. The impact of TPA and TNA are both different. The B value for TPA is 0.087, which means that purchase intention goes up with 0.089 with every increase of a single unit of positive affect. The impact of TNA is conversely. If TNA goes up with one, the
purchase intention decreases with 0.064. Both values are significant (both Sig values <0,05).
Figure 10.2 Coefficients
The significant impact of total positive affect and total negative affect attested from the regression analyses are strong support for hypothesis 6 and 7.
H6: There is a positive relationship between positive affect and purchase intention.
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Figure 10.3 shows a summary of all hypotheses including an overview of which hypotheses are accepted or rejected.
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6.0 Discussion
This research is conducted in order to get a better understanding of the influence of the retail environment on customer experience. The indoor weather variables are a part of the retail environment and no adequate research was done on this topic, while the total customer experience is getting more and more important. In this research, we found support for four hypotheses.
6.1 Significant influence of ’’indoor’’ weather variables on negative affect
Previous literature is not unanimous about a relationship between weather and mood. A possible explanation for the contradiction in literature is the way of measuring mood. In this research we looked at how mood arises in people’s minds. We found that both positive and negative affect plays an important role. So in order to measure mood, we made a clear distinction between positive affect and negative affect.
The results of this research support the outcomes of the research of Murray et al. (2010). Murray et al., found that higher levels of sunlight lead to a reduction of negative affect.
Goldstein (1972), Howarth and Hoffman (1984) and Cunningham (1979) showed that an increase in air pressure, temperature and intensity of light ensure an increase in mood. According to Watson et al. (2000) people have the best mood when negative affect is as low as possible and positive affect is as high as possible.
This research showed that there is a relationship between weather variables and peoples mood. The ’’indoor’’ weather conditions had a significant influence on negative affect. Negative affect is significantly higher under low indoor weather conditions than under high indoor weather conditions. The outcomes support H2.
6.2 Direct relation between positive and negative affect
According to Watson et al. (1988) positive and negative affect are in fact distinctive dimensions. This gives us the assumption that there is no direct relation between positive affect and negative affect. Our research found support for this assumption. So positive affect
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did not increase when negative affect goes down. This gives extra support for the idea that positive affect and negative affect are not opposites but rather completely different variables.
6.3 Affect influence on purchase intention
One of the most important outcomes of this research is, that we found a strong relation between mood and purchase intention. Which means that both low negative affect and high positive affect lead to more purchase intentions. So we found support for our sixth and seventh hypothesis
These outcomes are according to the results of other studies. Murray et al. (2010) suggests that people in a good mood are more likely to reward themselves. And Obermiller and Bitner (1987) suggest that a consumer evaluates goods better when he is in a good mood. Park, Lennon and Stoel, (2005) found that there is a relation between high positive affect and low negative affect and purchase intention. It also supports the research of Areni and Kim (1994). They found that people under bright light conditions buy significantly more that consumers under soft light conditions. So this implies that mood is essential in creation of purchase intention. According to Gosh (1990), purchase intention is a good predictor of actual purchase.
We showed that people’s mood (negative affect and positive affect), influences purchase intention. But a response on mood can be seen as direct and indirect. Direct response may be viewed as a conditioned response when there are direct linkages in associations in memory between mood-states and affective reactions (Griffitt and Guay,1969). So a direct reaction may be viewed as a conditioned response. Indirect response may include the influence of mood on information processing, or cognitive processing (Weiner 1979). Weiner also notes that behavior is the result of an interaction between cognition, feelings and expectations.
Doyle, Woodside and Michell (1979) suggest that post purchase evaluations are more
extensive in new buy situations than in straight rebuy situations. This could imply that a good mood is extremely important for straight rebuy situations because the response on mood is direct, and leads to an affective reaction. Mood is also important in new buy situations, but then cognition and feelings are more involved.
38 6.4 No influence of ’’indoor’’ weather variables on positive affect
No support was found for H1. So the assumption that high weather conditions leads to higher positive affect was not supported by the outcomes of this study. Despite the fact that we did not find support for H1, it is not possible to suppose that the results of the research of
Goldstein, Howarth and Hoffman are not true. It could be possible that an increase in mood is caused by a decrease in negative affect.
According to the research of LeDoux (1996) we already mentioned that it could be possible that the reduction of negative affect had a greater impact on peoples mood than the increase of positive affect. This is according to the idea that joy is not a mood or affect, but a natural state of being that occurs by the absence of negative affect (LeDoux, J.E., 1996).
So now that H1 is rejected and H2 is accepted, we can determine that the presence of high weather conditions does not make people more positive, but during absence of high weather conditions people get significantly more negative. This could be a designation to see weather variables as hygiene factors.
6.5 Negative affect as a hygiene factor
Frederick Herzberg (1987) investigated how you can activate people to do something for you and which way is most effective. He developed the motivation-hygiene theory. This theory suggests that factors involved in producing job satisfaction differ from the factors that lead to job dissatisfaction. Job satisfaction and job dissatisfaction are not opposites of each other. Dissatisfaction-avoidance factors are also named hygiene factors. So according to Herzberg, there are factors that motivate people And there are hygiene factors that not necessarily make people happy, however the absence of these factors do make people unhappy. His research showed that 81% of the factors that compute to job satisfaction are motivators and 69% of the factors that leads to dissatisfaction are hygiene factors. Examples of hygiene factors are salary and work conditions. These two factors do not make employees more happy, but the absence of them makes employees unhappy.
So considering the results of this researcher, we should make a difference between the impact of the weather variables on negative affect and positive affect. The absence of the high
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weather conditions does not make people significantly more happy. So according to Herzberg (1987) we can assume that indoor weather conditions are hygiene factors. And that the
weather variables need to be high in order to avoid dissatisfaction.
6.6 No direct influence of shopping intention
According to this research and the research of Velitchka D. Kaltcheva & Barton Weitz, (2006) we supposed that low weather variables have a stronger effect on negative affect for
recreational shoppers than for task orientated shoppers. We expected that in high weather conditions (high arousal), positive affect should increase for recreational shoppers. We also assumed that low weather condition are positively related with positive affect and negatively related with negative affect.
According to Alpert and Alpert (1990) mood has an impact on attitudes and behavior. Mood can be affected by many different variables (Gardner and Vandersteel,1984). Petty, Cacioppo and Schumann (1983) presume that consumers can receive information in two ways, the central route and the peripheral route. They presume that peripheral processing is likely under conditions of low involvement and that the central route is preferable in high involvement conditions. Music, humor and color seem to effect the mood of people positively under low involvement (Gorn, 1982). Consumers paired the conditioned stimulus (brand) with the unconditioned stimulus (sex, humor, etc.) and this produced a positive response with the brand. Task oriented shoppers are better approachable with central cues and low arousal (product information, clear shopping environment) and recreational shoppers are better approachable with peripheral cues and high arousal (sex, music and cozy shopping environment).
The results of this research show that shopping intention has an influence on people, but not in the way we expected. We found no support for H4 or H5, no significant difference in negative affect or positive affect was found between recreational shoppers in different weather conditions. Neither was there a significant difference in positive or negative affect for task-oriented shoppers in high or low weather conditions.
Our research found no support for H4 and H5. But what we found is a significant increase in negative affect for recreational shoppers in low weather variables pertaining to task-oriented shoppers in high weather variables. This means that a difference in shopping intention causes
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the lower score on negative affect. This implies that there is no impact of weather variables on mood if your customers are only recreational or task-oriented. Results showed some
difference between recreational shoppers in high and low conditions. But this difference was not significant (p=0.122). So influencing the indoor weather variables is only useful if consumers have no homogeneous shopping intention.
A theoretical explanation for the fact that we see no influence on positive affect for
recreational or task-oriented shoppers, is that according to Suzanne Piët (2009) sex, music and odor (peripheral cues) are connected with memory parts of the brain and weather influences are not. So high weather variables are no peripheral cues and are not the same as high arousal. Similarly, low weather variables are no central cues and not the same as low arousal.
According to the results low weather conditions are perhaps more connected to high arousal. For example, an environment with dimmed light (low light conditions) is often experienced as cozy.
But how could we explain these outcomes? A logical explanation for the increase in negative feeling is a lack of attention on the website or task. People who did not get a task have more time to focus on environmental factors. In the briefing before the experiment, participants were told that we wanted to investigate the indoor weather effects. So if people know that they are in a conditioned surrounding and they did not get an explicit task, they are more focused on the impact of the conditioned surroundings.
So the fact that positive affect is not influenced by shopping motivation is probably because low weather variables are in fact a mix of low and high arousal factors. The reason that there is an impact for negative affect is probably caused by people in the recreational group being more focused on the impact of the conditioned surroundings because they are not focused on an explicit task.
6.7 Homogeneity of variance
This research showed that weather conditions have a significant influence on negative affect. But when we drove the one-way ANOVA and we checked the homogeneity of variance for negative affect, the results of the Levene's test showed a significant value (p<0.05). This
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means that the variance is not homogeneous and that the outcomes are influenced by a few extreme outcomes. This seems detrimental, but the literature provides a logical explanation.
Faust, Weidmann and Wehmer (1974) found that 23% of all the respondents consider themselves as sensitive for weather variables (18% of the boy’s and 29% of all girls). In this research we did not control for gender but during the debriefing of the respondents, a few respondents (especially women) indicated that they were sensitive for weather influences. So in this research the significant value of the Levene’s test is not a weakness but we can see this as a confirmation of the research of Faust, Weidmann and Wehmer (1974).
6.8 Impact in model of customer experience
The initial motivation for investigating the indoor weather variables was found in the conceptual model of customer experience creation, as mentioned by Verhoef et al. (2009). Verhoef and his colleagues suggest that customer experience involves cognitive, affective, emotional, social and physical responses to the retailer. Customer experience is about the total experience. So customer experience is eventually a package of mental (emotional and
cognitive) and physical responses on the whole experience with the product or company.
This research shows that the indoor weather variables as part of the retail atmosphere has impact on customer experience. Important to note is that we can appoint good indoor weather variables as hygiene factors. So good indoor weather variables did not leads to a better customer experience, but the absence causes a decrease of customer experience. In order to get a better understanding in the way how customer experience works, the model could be separate into customer experience builders and customer experience decomposers.
6.9 Limitations and directions for future research:
Strength of this research is that participants are highly involved. If people are prepared to participate in the experiment, they do not drop out and keep focused on the questions.
A limitation of the research is that people’s affect is (positively) influenced before the experiment. Participants are selected by the convenience sampling strategy so most
