The influence of music volume in fast fashion stores: Consumers real time spent inside a store

i

The influence of music volume in fast fashion stores

Leonor Homem de Melo de Macedo Chaves Consumers real time spent inside a store

Dissertation presented as partial requirement for obtaining the master’s degree in Statistics and Information

Management

ii NOVA Information Management School

Instituto Superior de Estatística e Gestão de Informação Universidade Nova de Lisboa

THE INFLUENCE OF MUSIC VOLUME IN FAST FASHION STORES

by

Leonor Macedo Chaves

Dissertation presented as partial requirement for obtaining the master’s degree in Statistics and Information Management, with a specialization in Marketing Research and CRM

Advisor: Teodora Szabo-Douat, PhD

November 2022

iii

ACKNOWLEDGEMENTS

I would like to express my acknowledgements, first of all, to my advisor Teodora Szabo- Douat, for all the guidance and support provided throughout this process. For the help and advice whenever needed.

Secondly, a special acknowledgement to my parents and my brother for their unconditional support and for giving me the motivation and inspiration necessary to continue my journey. I also thank the rest of the family and everyone who directly or indirectly helped me to conduct this study.

Finally, I would like to thank all the people at the Xairel store. Without them, carrying out this study and doing my dissertation would not have been possible.

iv

ABSTRACT

Given the highly competitive fast fashion store market, these companies must learn more about who they are selling to, rather than simply wanting to sell something. Sensory marketing is critical for improving sales strategies. This dissertation seeks to better understand how music volume affects the real time that a customer spends inside a store. Following a review of the literature on the most significant antecedents, a hypothesis is presented based on the background music and the time spent inside the store. For the research, the decibel and time variables were used. Regarding the methodology, I conducted a field experiment at Xairel – a store located in Principe Real, Lisbon. A final sample of 120 customers was obtained. Data processing was carried out through a statistical analysis with SPSS. An analysis of variance - One-Way ANOVA was used to test for statistically significant differences between the three decibel levels investigated. This research contributes to the fast fashion store market by elucidating potential areas for improvement in the sensory marketing field.

KEYWORDS

Atmospheric marketing; Consumer behavior; Fast fashion; Music volume

v

INDEX

1. Introduction ... 1

1.1. Background and problem identification ... 1

1.2. Study Relevance and importance ... 2

1.3. Study Objectives ... 3

2. Literature review ... 4

2.1. Atmospherics ... 4

2.1.1. Sensory marketing ... 4

2.2. Music effect ... 5

2.2.1. Measurement: Decibel (dB) ... 6

3. Methodology ... 7

3.1. Conceptual model ... 7

3.2. Pre-test ... 8

3.2.1. Pre-test results and discussion ... 9

3.3. Data Collection ... 10

4. Results and discussion ... 12

5. Conclusion ... 18

6. Limitations and recommendations for future works ... 20

7. Bibliography ... 21

8. Appendix ... 24

vi

LIST OF FIGURES

Figure 1. Conceptual framework of sensory marketing (Krishna, 2011, p.335) ... 5

Figure 2 - Theoretical model ... 7

Figure 3 - Normal Q-Q plots ... 13

Figure 4 - Time and LnTime histogram ... 14

vii

LIST OF TABLES

Table 1 - Decibel levels. Source: Boomspeaker ... 6

Table 2 - Pre Test Outcomes ... 9

Table 3 - Decibel Chart (From: Daniel (2007)) ... 10

Table 4 - OSHA's Permissible Noise Exposure Limits ... 11

Table 5 - Skewness and Kurtosis figures ... 13

Table 6 - Kolmogorov- Smirnov and Shapiro-Wilk Tests ... 13

Table 7 - Statistics Time and lnTime ... 14

Table 8 - Tests of Between-Subjects Effects ... 15

Table 9 - Estimated Marginal Means ... 15

Table 10 - Pairwise Comparisons ... 16

Table 11 - Univariate Tests (F-tests) ... 17

Table 12. Sample 48.8 dB - 65 dB ... 24

Table 13. Sample 80 dB - 85 dB ... 25

Table 14. Sample 95 dB - 97 dB ... 26

Table 15 - Time frequency ... 27

Table 16 - LnTime frequency ... 30

viii

LIST OF ABBREVIATIONS AND ACRONYMS

dB Decibel

Tu Transmission Unit

BCa Bias-corrected and accelerated

1

1. INTRODUCTION

1.1. B

Marketing, as we know and use it today, is the result of a deconstruction and a better perception of the general marketing studied in the early twentieth century by authors such as Arch W. Shaw (1951) and Ralph Starr Butler (1916). TEThis growth also stems from the improvement of all the current technologies (Vassileva, 2017) and the emergence of new studies that prove the importance of connecting all aspects of a brand with its consumer. The more personal the relationship is, or seems to be, the better the results are (Albert & Merunka, 2013).

The conscious planning of atmospheres to contribute to the buyer’s purchasing propensity has been a very important marketing tool for decades. It was first defended and talked about by Kotler (1973). When buying a product, consumers don’t just focus on the product itself, they focus on the total product. This total product being the combination of services, warranties, packaging, advertising, and other features such as the place. More specifically, the atmosphere of the place (Kotler Philip, 1973).

These studies opened a wide window for others that wanted to better understand the new ways of making the most out of marketing in their industries.

Rúa (2015) explains the difference between traditional marketing and sensory marketing based on the rationality of the former in a comparison with the major importance of experiences and emotions for consumers who are driven more by impulse than by reason (Strack et al., 2006). Unlike traditional marketing, that says that consumer behaviors are based on satisfying their needs according to the appropriate offer provided, sensory marketing has emotion as its central axis and centers the buying process on the experience of the sensations connected to this process (de Garcillán López-Rúa, 2015). With this said, we can state that sensory marketing allows us to influence consumer perception and behavior in their buying process (Berčík et al., 2020).

If it is through the senses and experiences of sensory marketing that the consumer will best meet his or her needs, it is also through the five senses that each person perceives the environment that is most attractive to him or her (Sliburytė & le Ny, 2017).

Some studies argue that multi-sensory cues in the store atmosphere make it possible to increase the time spent shopping by creating a pleasant environment (Berčík et al., 2020). There are some sensory factors that we can change to provide the most pleasant environment for our consumer.

Wakefield and Baker (1998) found that all environmental factors (design, music, layout, and décor) are related to excitement and desire to stay at the mall. Morrison et al. (2011) focused only on aroma and music and discovered that these have a positive effect on mood states and satisfaction levels - state of mind. Herrmann et al. (2013) studied the effect that scent has on sales. Also, the price perception of a customer according to the color and lighting was studied by Babin et al. (2003).

In many of the studies conducted, a large part of these focus on music and all the aspects implicit in it, including the type, tempo and volume, among other factors (Milliman, 1986; Srinivasan &

Mukherjee, 2012). Most of these studies tried to understand how this could affect the consumer's mind. It could be either through the influence of happy or sad music at the moment of purchase

2 (Sackeim et al., 2008),or even the fact that there is or is no background music at all (Srinivasan &

Mukherjee, 2012).

Real case studies were conducted in restaurants (Milliman, 1986) and in-store behavior (Gorn, 1982; Kellaris & Altsech, 1992; Milliman, 1986) to better understand what to do and how to improve the way we operate. Milliman (1986) showed us that when slow-tempo music was used, the customers stayed longer, ate the same amount of food but consumed more alcoholic beverages. When a faster- tempo was used, some groups left the restaurant before they were even assigned a table.

Most companies have already realized the importance of sensory marketing in their business and have put it into action. With the help of existing and proven studies, companies will be able to benefit from being able to influence with the consumer's mind without them realizing it. For example, getting the customer to stay longer in the store in a way that they are not aware of (Ridgway & Bloch, 1990).

1.2. S

R

With the growth in the retail segment and increased competition, retailers should be doing everything they can to make sure they get more customers in their store and for them to stay inside longer (Srinivasan & Mukherjee, 2012). The use of scientific knowledge generated for the business sector shows an increasing application of sensory marketing techniques in commercial establishments.

Mainly, marketing managers use different techniques to enhance the consumer experience through their senses (Pablo et al., 2022). To invest in background music as part of a place’s atmosphere is inevitable (Srinivasan & Mukherjee, 2012).

This study will show how the volume of music will influence the real time a customer stays inside the store. Time that could increase the number of things the client buys (R. F. Yalch & Spangenberg, 2000). An important study was previously done for brands where the volume of music in the store was also studied but according to the perishable time and the gender of the person (Kellaris & Altsech, 1992). My study will complement the studies that have already been conducted on the five senses especially in music and with a greater focus on its volume.

Considering this, fashion retailers should create complete sensory experiences and not just focus on one of the senses. Their strategies and tactics should involve all five senses, since their joint implementation has a decisive influence on buying behavior (Hultén, 2011). For a better use of the results of this study, other studies about atmosphere marketing should be used to complement this research. Although my study is mainly directed at fast fashion companies, other retail outlets may acquire knowledge that will be beneficial to them.

3

1.3. S

O

This investigation’s primary objective is to better understand how the volume of music in a fast fashion store will influence the real time a consumer stays inside the store. Through this information I will be able to see how the volume of the music can increase the amount of things the customer buys and thus increase a store’s profit (R. F. Yalch & Spangenberg, 2000)

Through the information collected and researched I have found a gap in the impact of the volume of the music in fast fashion stores in the real time spent inside a shop. Through this gap I formulated my research question - “What impact has the volume of the music in fast fashion stores in the time spent inside a shop?” - that will be explored in the following chapters.

4

2. LITERATURE REVIEW

2.1. A

The atmosphere of a space is always present and is a way of assessing the quality of the space we are in (Kotler Philip, 1973). “…atmospherics is the effort to design buying environments to produce specific emotional effects in the buyer that enhance his purchase probability” (Kotler, 1973, p.50).

Kotler (1973) showed that the total product, the atmosphere of the place – services, warranties, packaging, advertising financing, among others - influences the buyer in the purchase decision more than the actual product itself. These environmental changes that are hardly noticed or consciously perceived by the customer, will cause a change in the consumers' behaviors when in contact with these stimuli in the stores (Turley & Milliman, 2000). These small changes are decisive for the success or failure of a company (Bitner, 1990).

Spangenberg et al. (1996) observed that atmospheric psychology is inspired by the stimulus- organism response (S-O-R) paradigm. The atmosphere is the stimulus (S) that triggers a consumer evaluation (O) and causes some behavioral response (R) (Donovan, 1982; Mehrabian, and Russell, 1974).

2.1.1. Sensory marketing

Since sensory marketing, the marketing that engages the consumers (Krishna, 2012), is a silent area in communication, only recently financial supporters have begun to give due importance and attention to its results (Kotler Philip, 1973). The main sensory channels used are the visual dimension, the aural dimension – volume, pitch -, the olfactory dimension and the tactile dimension (Kotler Philip, 1973). All these main sensory channels will bring feelings and emotions to the consumer about the brand. These thoughts will be reflected in the image of the brand in the mind that will define the brand for that person (Grönroos, 2008).

An SM model takes as its starting point the human senses that then result in a multi-sensory brand experience. In other words, each consumer will use the logic they have in their knowledge. This logic is used to form emotional, behavioral, sensorial, cognitive, and symbolic values (Schmitt, 1999).

According to Hultén (2011) the SM model will highlight the multi-sensory experience of a brand in its differentiation and the positioning of the brand in the human mind in an image shape.

5

2.2. M

Music is one of the most highly controllable factors in the atmosphere. We can manage it from loud to soft, from fast to slow, from vocal to instrumental, from heavy rock to light rock, and from classical to urban contemporary (Milliman, 1986). It has been manipulated both in its structural aspects such as tempo (rhythm, tempo, phrasing); pitch (melody, mode, harmony) and texture (timbre, orchestration, and volume), and in its affective elements such as liking, familiarity and different kinds (Jain & Bagdare, 2011).

The studies conducted on the effect of music have some interesting results that will help in my study.

The type of music has the power to control customers' perception of their shopping time. While shopping, the type of music according to the shopper’s age influences the time they spend shopping.

Also, this variable is used in restaurants in order to increase their turnover. Fast-tempo music is used during busy lunch hours to increase revenue, and during quieter times, slow-tempo music is used so that customers consume more (R. Yalch & Spangenberg, 1990).

Music can also be used as a defense mechanism for problems that are not always manageable.

This is the case with queues. Music can give a false perception of the length of a queue (Jacoby et al., 1976).

According to the Kellaris and Altsech (1992) study the gender factor is not a good moderator when evaluating the volume on music in store perishable time. The effect of volume may cause gender difference but only at very high fades. A high decibel volume that is not used in stores and would thus have little importance for marketing studies (Kellaris & Altsech, 1992).

Figure 1. Conceptual framework of sensory marketing (Krishna, 2011, p.335)

6 2.2.1. Measurement: Decibel (dB)

This measure was first named Transmission Unit (Tu) by Bell system communication engineers (Johnson, 2012)

Sound is a sensory perception and, depending on the pattern of sound waves generated, it is recognized as being music, speech, or any of the types of environmental noises to which we are continuously exposed. Of the four characteristics that sound has, we will study intensity and pressure (measured through decibels) (Pope, 2010). The sound can be measured in two ways: through frequency – Hertz (Hz) or through amplitude – decibel (dB). In this work we will study music volume, so to do so, we need to explore the amplitude of the sound (the more amplitude the sound has, the louder it is) (Clason, 2021).

Table 1 - Decibel levels. Source: Boomspeaker

7

3. METHODOLOGY

3.1. C

The study aims to understand how the volume of background music in a fast fashion store will influence the real time consumers stay inside the store. Taking into consideration through the studies that have been written that, in fact, music alters consumer buying behavior (Srinivasan & Mukherjee, 2012; Wakefield & Baker, 1998)

Therefore, the theoretical model proposed (Figure 4.) shows how background music affects the time spent in store (Srinivasan & Mukherjee, 2012) this time using as moderator the music’s volume.

Figure 2 - Theoretical model

Accordingly, there is one hypothesis that is defined as:

H1: The lower the volume of the background music the more time consumers stays in-store.

After analyzing all of the collected data, I can conclude that the hypothesis under consideration is significant. Customers stay longer inside the store when the background music has a lower decibel range (48.8 dB - 65 dB).

8

3.2. P

-

In order to achieve the best results possible, a pre-test was conducted through a field experiment.

Instead of conducting a questionnaire about what consumers thought about the volume of music in the different fast fashion stores, I decided to conduct a field experiment in order to find out what the real decibel values were, and not what the customers thought about the volume. “(…) sometimes behavioral science is ignored. The reason behind this is that advertisers often ask consumers directly about their motivations. Based on premise that what consumers say and do are aligned” (Shotton, 2018, p.5).

This pre-test was conducted in fourteen of the best-known fast fashion stores in Cascais Shopping center. The main purpose of this pre-test was to understand the decibel level of each store. This information along with some studies helped me to define the decibel values I should use to set the three levels of music volume. To get to these values, I recorded each store for about 20 to 50 seconds in which I walked around. With these records I was able to access the minimum, the maximum, the average, and the highest peak decibel levels. All the records were made on the same weekday.

To measure the decibels, I used the Decibel X app, which is one of the best applications to read these values according to Healthy Hearing, a hearing clinic in the U.S. This app is pre-calibrated and has a standard measurement range from 30 dB to 130 dB. It uses the smartphone microphone to detect sound and display it in decibels.

9 3.2.1. Pre-test results and discussion

In Table 1. we can find all the results obtained and which fast fashion stores brands were studied in the pre-test performed. To better study the results I created a total average for each of the factors present in the table (min dB, max dB, peak dB, and also for the average dB of each brand).

Table 2 – Pre-Test Outcomes

While reading the following results, we need to take into consideration that the size of the stores and background noise will influence the decibels detected. In this way the results are not the neatest.

Fast fashion store Min (dB) Max (dB) Peak (dB) Average (dB)

Mango 48.3 59.2 63.7 52.9

Zara 55.1 67.6 71.7 59.8

Bimba Y Lola 49.9 63.8 67.2 57.2

Springfield 49.3 61.2 65.4 54.8

Pull and Bear 50.6 60.9 63.5 56.2

Stradivarious 49.2 70.0 74.2 60.1

Oysho 51.3 58.3 61.8 55.1

Bershka 48.8 68.2 72.7 60.1

H&M 42.0 57.6 62.5 50.3

C&A 50.0 61.1 62.8 55.5

Parfois 49.3 63.7 67.9 57.3

Natura 49.0 72.7 77.0 60.5

Cortefiel 44.0 57.6 65.7 51.4

Massimo Dutti 46.3 62.9 67.5 55.9

Average (dB) 48.8 63.2 67.4 56.2

10 From Table 1. we can conclude that all the decibel values vary between 40 dB and 80 dB. We can also observe that in the column “Peak” we usually have higher values. These values are not of great importance in the study in question because they are merely values obtained in a very short time.

Looking at the table as an overview, we can see that the minimum average decibel value is 48.8 (identical in value to the Bershka store) and the maximum average value is 63.2 dB (the store with the closest value to this is Massimo Dutti – 62.9). The average of the averages of all factors of each brand is 56.2 dB.

3.3. D

C

To collect the data, I observed how long customers stayed inside Xairel¹'s store over a period of three days, each day with a different range of decibels. The three decibel intervals were defined after analyzing the pre-test data and linking it to some research.

Table 3 - Decibel Chart (From: Daniel (2007))

By looking at Table 2., studied by Daniel (2007), I defined that for the music to be audible it should have values in the range of 50 dB to 65 dB, the equivalent to a normal conversation. Since in the pre- test the minimum decibel value in a store was 48.8 dB, I designed the first interval for further study based on this information. This range was set at 48.8 dB to 65 dB.

1 Xairel, portuguese brands. https://www.xairel.pt/

11 When researching the values to build the second interval, I kept in mind that I didn't want the third interval to exceed 97 decibels. Thus, I defined the values for the second interval to be between 80 dB and 85 dB.

For the third range I chose values between 95 dB and 97 dB. I chose 97 dB as the limit because according to OSHA (1988), the time limit for exposure to this volume is three hours. To collect my data, I could never stay less than three hours in a store, so the decibels really couldn't be higher than this value.

The three defined intervals do not have the same dimension because the decibels are non- linear(Roberts, 2003). The intervals also have a distance between them, so that if any song that was playing has a "Peak" the decibels wouldn't spread on to the next interval.

Inside the Xairel store I spent three hours every day measuring the customers' time inside. It was these three hours every day, because in the last break I could not spend more hours than that. Within this time, I did not interact with the customers in any way, and they are not identified in my study. This daily period was always observed at the same time of day on the three different days.

After the first day, in which I gained a result of forty customers in three hours, this number was chosen as the sample for all the days. This gave a total sample of 120 people in the store.

Table 4 - OSHA's Permissible Noise Exposure Limits

12

4. RESULTS AND DISCUSSION

A one-way between groups ANOVA analysis of variance was performed in order to assess whether there were differences in time within the store between the three different decibel levels. In order to evaluate the assumption of normality of the data I used three methods: the Skewness and Kurtosis, the Kolmogorov- Smirnov and Shapiro-Wilk tests and the Normal Q-Q plots.

To clean up the data, I organized the three decibel intervals into experimental conditions, as follows:

1 = 48.8 dB – 65 dB 2 = 80 dB – 85 dB 3 = 95 dB – 97 dB

13 In table 5. we can observe, according to the rule of thumb, that the Skewness in group 1 and 2 and the Skewness and Kurtosis in group 3 exceed twice the standard error. Which shows that we don't have normality.

Table 6 - Kolmogorov- Smirnov and Shapiro-Wilk Tests

In Table 6, to verify that normality exists, the significance levels have to be greater than 0.05.

This only occurs for variables 1 and 2 in the Kolmogorov-Smirnov test. This means that the data do not have a normal distribution (Kolmogorov-Smirnov (3) = 0.23, p < 0.01 ; Shapiro-Wilk (1) = 0.92, p < 0.09;

Shapiro-Wilk (2) = 0.90, p < 0.03; Shapiro-Wilk (3) = 0.68, p < 0.01).

Table 5 - Skewness and Kurtosis figures

Figure 3 - Normal Q-Q plots

14 When looking at the Quartile-Quartile normal graphs (figure 3) we see that the points are not close to the line, which means that they are not normally distributed.

After analyzing these three methods we concluded that our data does not have a normal distribution. More specifically, time is not normally distributed. This means that we will have to perform some transformations. In this case we will perform a simple logarithmic transformation of time (with ln). Following this, we will conduct analyses with the variable lnTime as the dependent variable.

Table 7 - Statistics Time and lnTime

In table 7. we can see the statistics of time and lnTime. Through the variables Skewness and Kurtosis we can say that the simple logarithmic transformation of time now gives us the variable lnTime with a normal distribution. Again, with the rule of thumb, we can check that the lnTime values of Skewness and Kurtosis don’t exceed twice their standard error, in this way, they present normality.

Skewness LnTime: - 0.5 < 0.44 Kurtosis LnTime: - 0.35 < 0.88

Figure 4 - Time and LnTime histogram

1 2

15 We can confirm the information from the graph above by looking at the time histogram (1) and the new logarithmic transformation histogram – lnTime (2). According to the results, the first graph does not have a normal distribution. In graph 2, we can see that the new data produces a graph with a normal distribution. With the normality of time established, we can begin data analysis using lnTime instead of time as the dependent variable.

Table 8 - Tests of Between-Subjects Effects

When looking at the tests of between- subjects effects (table 8), the omnibus ANOVA, we can observe that there is a significant interaction between lnTime and the decibel levels (p < .001), at F (1, 117) = 88.47, p < .001. With these findings, we can now assess the post-hoc tests (Pairwise Comparisons) to better understand the interaction between the variables.

In Table 9. we can see that in Table 1. the estimated marginal Grand means is:

[(M (DbConditions, lnTime) = 0.87, IC 95% (0.69 – 1.05)]

Table 9 - Estimated Marginal Means

1 2

16 We can also observe the estimated marginal means for the three decibel condition ranges with the dependent variable lnTime:

Range 1

[(M (DbCondition 1, lnTime) = 1.12, IC 95% (0.81 – 1.44)]

Range 2

[(M (DbCondition 2, lnTime) = 0.83, IC 95% (0.51 – 1.14)]

Range 3

[(M (DbCondition 3, lnTime) = 0.66, IC 95% (0.34 – 0.98)]

From the values in Table 9, we can observe that the highest average time inside the store happens in decibel range 1 (when the music volume is between 48.8 dB and 65 dB). And the lowest average time inside the store is when the music volume is in the third interval. That is, the highest volume (between 95 dB and 97 dB).

Table 10 - Pairwise Comparisons

In Table 10 I will analyze the actual comparisons amongst the three intervals. From the table I can see that the mean difference between interval 1 and interval 3 is significant. Proven by Sig < 0.05. This is in fact the only comparison in the table that is statistically significant. When checking the value of

A

B

17 mean differences (A and B) we noticed that the value of A is positive and that of B is negative. This means that in interval 1 (condition A) the customers stayed significantly longer inside the store.

t (117) = 0,46 / 0,23 = 2, p < 0,05

Table 11 - Univariate Tests (F-tests)

In Table 11. we can observe the F-tests that will analyze the effect of the decibel condition.

According to the results of the table, Sig > 0.05, meaning that there is no significant effect of group.

[(ANOVA F (2, 117) = 4.42, p > 0,05)].

18

5. CONCLUSION

The goal of every business is to keep its clients and retain them over the coming years. Customers are every company's most vital component, since without them, there would be no business. Customer happiness needs to come first for a business to succeed. A business must understand how to satisfy clients and what those customers' wants are.

Sensory marketing is an extremely important aspect in the development and improvement of a business. In the area of my study (fast fashion stores), to make better use of this type of marketing it is very important to know all the areas that can be worked on. Even more important, however, is to know the customer and to understand how they react to different stimuli. In this way, just by changing the type of music, the smell of the store, or even the volume of the music we can encourage the customer to stay inside longer and make more purchases, as we can direct them to the newest items that are in the store - the new collection.

The primary goal of this dissertation was to analyze how the volume of music in a fast fashion store would influence the real time customers would stay inside the store. I had planned to conduct the study in a fast fashion store, but after receiving no responses, I looked for a store with music and traffic to collect my sample. I then found Xairel, a Portuguese street store located in Príncipe Real, Lisbon.

I had a total of 120 customers (40 per day) over the three days I was in the store, each day with its own decibel range. Throughout the study, I looked at two variables: time (the dependent variable) and decibels (the independent variable).

We can see the initial averages by looking at the sample values, but we can't draw any conclusions because we haven't yet run our statistical tests. According to the first results, the first day's average time was 4'43 minutes, the second day's average time was 3'17 minutes, and the third day's average time was 3'39 minutes. On a non-statistical level, this means that the average customer’s time was longer on the first day, with decibels ranging from 48.8 to 65, than on the other two days. The average time of the clients was the shortest on the second day, with decibels ranging from 80 to 85.

The average on the third day (with a range of 95 dB to 97 dB) was in the middle of the other two, but closer to the second.

Since my study had two variables, and because I wanted to compare the means of the three decibel intervals, I used a One-Way ANOVA method. To run the ANOVA, I first tested whether it had a normal distribution or not. To analyze normality, we used the Skewness and Kurtosis tests, the Kolmogorov-Smirnov and Shapiro-Wilk tests, and the Normal Q-Q plots. When I looked at the results, I realized that the sample I had did not have a normal distribution, more specifically the time variable was not normally distributed.

In order to restore time normalcy, I had to perform a simple logarithmic transformation of time (with ln). With this transformation, I was able to adjust my data to present a normal distribution.

With the normal distribution solved, I was able to run the analysis with the new dependent variable (lnTime) and determine whether my data was statistically significant, or not. With a

19 significance level of less than 0.05 in the table Tests of Between-Subjects Effects, my analysis is statistically significant, with a significant interaction between lnTime and the decibels levels.

With the proven significance, I performed a Pairwise Comparison to understand the actual comparisons I could make between the three decibel ranges. In this table I found that only the comparison between interval 1 (48.8 dB - 65 dB) and interval 3 (95 dB - 97 dB) was statistically significant, with a significance level lower than 0.05 (Sig = 0.042). With this information I can conclude that in the interval with the lowest music volume (48.8 dB – 65 dB) the customers stayed longer inside the store, which would lead them to buy more according to the study made by Yalch and Spangenberg (2000).

20

6. LIMITATIONS AND RECOMMENDATIONS FOR FUTURE WORKS

It may be relevant to state some of the limitations that I have found during the realization of this dissertation in order to recommend possible lines for future investigations. Firstly, since my sample was collected through a field experiment, I needed a store to gather all the values. I tried for several months to contact the fast fashion brands that I had previously collected data from for my pre-test.

But I never received an answer from any of them. Due to this, I looked for a store that also had music in it. The fact that this store was smaller than a fast fashion store made it more complicated to get a larger sample. Probably with a larger sample I could have drawn even more insights.

Secondly, the fact that I was not able to go to a store with the dimensions of the stores I had studied in my pre-test may have also influenced the results. The store where I conducted my study has a smaller footprint and is located in a busy area on the street, not in a shopping center.

Therefore, one recommendation for future work is to be aware that it is often complicated to contact very large brands, with various levels of bureaucracy to navigate in between. The time we have is out of our hands when a number of authorizations are needed.

This study can be used as inspiration for future studies, perhaps even on the same theme but carried out in a larger store with more traffic, where it would be possible to collect a larger number of samples at the same time.

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7. BIBLIOGRAPHY

Albert, N., & Merunka, D. (2013). The role of brand love in consumer-brand relationships. Journal of Consumer Marketing, 30(3), 258–266. https://doi.org/10.1108/07363761311328928

Arch W. Shaw. (1951). SOME PROBLEMS IN MARKET DISTRIBUTION.

Babin, B. J., Hardesty, D. M., & Suter, T. A. (2003). Color and shopping intentions: The intervening effect of price fairness and perceived affect. Journal of Business Research, 56(7), 541–551.

https://doi.org/10.1016/S0148-2963(01)00246-6

Berčík, J., Mravcová, A., Gálová, J., & Mikláš, M. (2020). The use of consumer neuroscience in Aroma marketing of a service company. Potravinarstvo Slovak Journal of Food Sciences, 14, 1200–1210.

https://doi.org/10.5219/1465

Bitner, M. J. (1990). Evaluating Service Encounters: The Effects of Physical Surroundings and Employee Responses. In Source: Journal of Marketing (Vol. 54, Issue 2).

Daniel, E. (2007). Noise and hearing loss: A review. In Journal of School Health (Vol. 77, Issue 5, pp.

225–231). https://doi.org/10.1111/j.1746-1561.2007.00197.x

de Garcillán López-Rúa, M. (2015). Persuasión a través del marketing sensorial y experiencial Persuasion through Sensory and Experiential Marketing. No. Especial, 31, 463–478.

Donovan, R. (1982). Store Atmosphere: An Environmental Psychology Approach.

https://www.researchgate.net/publication/248766608

Gorn, G. J. (1982). The Effects of Music in Advertising on Choice Behavior: A Classical Conditioning Approach. In Source: Journal of Marketing (Vol. 46, Issue 1). Winter.

Grönroos, C. (2008). Service logic revisited: Who creates value? And who co-creates? European Business Review, 20(4), 298–314. https://doi.org/10.1108/09555340810886585

Herrmann, A., Zidansek, M., Sprott, D. E., & Spangenberg, E. R. (2013). The Power of Simplicity:

Processing Fluency and the Effects of Olfactory Cues on Retail Sales. Journal of Retailing, 89(1), 30–43. https://doi.org/10.1016/j.jretai.2012.08.002

Hultén, B. (2011). Sensory marketing: The multi-sensory brand-experience concept. European Business Review, 23(3), 256–273. https://doi.org/10.1108/09555341111130245

Jacoby, J., Szybillo, G. J., & Berning, C. K. (1976). Time and Consumer Behavior: An Interdisciplinary Overview. Journal of Consumer Research, 2(4), 320. https://doi.org/10.1086/208644

Jain, R., & Bagdare, S. (2011). Music and consumption experience: A review. In International Journal of Retail and Distribution Management (Vol. 39, Issue 4, pp. 289–302).

https://doi.org/10.1108/09590551111117554 Johnson, K. S. (2012). NOTE Decibel Tables.

22 Kellaris, J., & Altsech, M. (1992). The Experience of Time as a Function of Musical Loudness and

Gender of Listener. Association for Consumer Research, 19(Advances in Consumer Research), 725–729.

Kotler Philip. (1973). Atmospherics as a marketing tool. Journal of Retailing, 49, 48–64.

Krishna, A. (2012). An integrative review of sensory marketing: Engaging the senses to affect perception, judgment and behavior. In Journal of Consumer Psychology (Vol. 22, Issue 3, pp.

332–351). https://doi.org/10.1016/j.jcps.2011.08.003

Milliman, R. E. (1986). The Influence of Background Music on the Behavior of Restaurant Patrons. In Source: Journal of Consumer Research (Vol. 13, Issue 2).

https://www.jstor.org/stable/2489234?seq=1&cid=pdf-

Morrison, M., Gan, S., Dubelaar, C., & Oppewal, H. (2011). In-store music and aroma influences on shopper behavior and satisfaction. Journal of Business Research, 64(6), 558–564.

https://doi.org/10.1016/j.jbusres.2010.06.006

Pablo, P., Dueñas, M., & Gómez, D. (2022). Application of sensory marketing techniques in fashion shops: the case of Zara and Stradivarius. Vivat Academia, 155, 17–32.

https://doi.org/10.15178/va.2022.155.e1392

Pope, D. (2010). Decibel levels and noise generators on four medical/surgical nursing units. Journal of Clinical Nursing, 19(17–18), 2463–2470. https://doi.org/10.1111/j.1365-2702.2010.03263.x Ridgway, N. M., & Bloch, P. (1990). Shopping Motives, Transient Emotional States and Retail

Outcomes Compulsive Buying View project.

https://www.researchgate.net/publication/236965035 Roberts, B. L. (2003). The Decibel Scale.

Sackeim, H. A., Prudic, J., Nobler, M. S., Fitzsimons, L., Lisanby, S. H., Payne, N., Berman, R. M., Brakemeier, E. L., Perera, T., & Devanand, D. P. (2008). Effects of pulse width and electrode placement on the efficacy and cognitive effects of electroconvulsive therapy. Brain Stimulation, 1(2), 71–83. https://doi.org/10.1016/j.brs.2008.03.001

Schmitt, B. (1999). Experiential Marketing. Journal of Marketing Management, 15(1–3), 53–67.

https://doi.org/10.1362/026725799784870496

Sliburytė, L., & le Ny, J. (2017, May 23). The Influence of Sensory Marketing: a Study of Atmospheric Factors and Consumer Response. https://doi.org/10.3846/cbme.2017.109

Spangenberg, E. R., Crowley, A. E., & Henderson, P. W. (1996). Improving the Store Environment: Do Olfactory Cues Affect Evaluations and Behaviors? In Source: Journal of Marketing (Vol. 60, Issue 2).

Srinivasan, S. R., & Mukherjee, P. N. (2012). Effect of Background Music on Consumer Shopping Behavior. In The Business & Management Review (Vol. 2, Issue 1).

23 Strack, F., Werth, L., & Deutsch, R. (2006). Reflective and impulsive determinants of consumer

behavior. In Journal of Consumer Psychology (Vol. 16, Issue 3, pp. 205–216). Elsevier Inc.

https://doi.org/10.1207/s15327663jcp1603_2

Turley, L. W., & Milliman, R. E. (2000). Atmospheric Effects on Shopping Behavior: A Review of the Experimental Evidence. Journal of Business Research, 49, 193–211.

Vassileva, B. (2017). Marketing 4.0: How Technologies Transform Marketing Organization. In Óbuda University e-Bulletin (Vol. 7, Issue 1). http://www.ey.com/Publication/vwLUAssets/EY-

Wakefield, K. L., & Baker, J. (1998). Excitement at the Mall: Determinants and Effects on Shopping Response.

Yalch, R. F., & Spangenberg, E. R. (2000). The Effects of Music in a Retail Setting on Real and Perceived Shopping Times. Journal of Business Research, 139–147.

Yalch, R., & Spangenberg, E. (1990). Effects of store music on shopping behavior. Journal of Consumer Marketing, 7(2), 55–63. https://doi.org/10.1108/EUM0000000002577

24

8. APPENDIX

Annexe I – Sample results

Table 12. Sample 48.8 dB - 65 dB

Customers Time (min)

1 6.21

2 5.03

3 4.41

4 5.35

5 1.06

6 3.23

7 6.32

8 0.29

9 6.17

10 5.48

11 6.05

12 13.08

13 5.2

14 8.45

15 0.4

16 12.03

17 5.13

18 1.16

19 0.23

20 3.52

21 5.26

22 6.53

23 3.16

24 1.47

25 1.18

26 2.55

27 0.35

28 2.29

29 1.58

30 2.02

31 11.4

32 5.01

33 1.46

34 9.47

35 6.23

36 3.46

37 2.4

38 3.42

39 2.04

40 7.13

25 Table 13. Sample 80 dB - 85 dB

Customers Time (min)

1 8.39

2 0.31

3 3.12

4 8.16

5 4.14

6 7.34

7 3.1

8 3.38

9 1.44

10 3.27

11 2.32

12 2.44

13 1.43

14 1.54

15 1.03

16 1.18

17 3.05

18 2.52

19 1.28

20 0.3

21 7.14

22 4.38

23 0.53

24 5.04

25 4.12

26 1.45

27 3.19

28 2.09

29 8.56

30 0.31

31 3.47

32 4.10

33 1.47

34 0.28

35 2.46

36 6.34

37 2.21

38 4.19

39 1.54

40 4.36

26 Table 14. Sample 95 dB - 97 dB

Customers Time (min)

1 1.35

2 0.53

3 7.2

4 21.36

5 1.06

6 0.52

7 0.42

8 1

9 3.3

10 3.37

11 4.29

12 4.09

13 0.48

14 1.16

15 1.18

16 3.16

17 2.28

18 6.22

19 0.47

20 8.49

21 1.32

22 1.23

23 15.35

24 7.5

25 4.35

26 3.26

27 0.85

28 2.2

29 3.12

30 0.22

31 2.44

32 0.39

33 2.08

34 5.31

35 0.58

36 1.04

37 2.25

38 7.05

39 2.43

40 0.56

27 Annexe II – Frequency tables

Table 15 - Time frequency Time

Frequency Percent Valid Percent

Cumulative Percent

Valid

.22 1 .8 .8 .8

.23 1 .8 .8 1.7

.28 1 .8 .8 2.5

.29 1 .8 .8 3.3

.30 1 .8 .8 4.2

.31 2 1.7 1.7 5.8

.35 1 .8 .8 6.7

.39 1 .8 .8 7.5

.40 1 .8 .8 8.3

.42 1 .8 .8 9.2

.47 1 .8 .8 10.0

.48 1 .8 .8 10.8

.52 1 .8 .8 11.7

.53 2 1.7 1.7 13.3

.56 1 .8 .8 14.2

.58 1 .8 .8 15.0

.85 1 .8 .8 15.8

1.00 1 .8 .8 16.7

1.03 1 .8 .8 17.5

1.04 1 .8 .8 18.3

1.06 2 1.7 1.7 20.0

1.16 2 1.7 1.7 21.7

1.18 3 2.5 2.5 24.2

1.23 1 .8 .8 25.0

1.28 1 .8 .8 25.8

1.32 1 .8 .8 26.7

1.35 1 .8 .8 27.5

1.43 1 .8 .8 28.3

1.44 1 .8 .8 29.2

1.45 1 .8 .8 30.0

1.46 1 .8 .8 30.8

1.47 2 1.7 1.7 32.5

1.54 2 1.7 1.7 34.2

1.58 1 .8 .8 35.0

28

2.02 1 .8 .8 35.8

2.04 1 .8 .8 36.7

2.08 1 .8 .8 37.5

2.09 1 .8 .8 38.3

2.20 1 .8 .8 39.2

2.21 1 .8 .8 40.0

2.25 1 .8 .8 40.8

2.28 1 .8 .8 41.7

2.29 1 .8 .8 42.5

2.32 1 .8 .8 43.3

2.40 1 .8 .8 44.2

2.43 1 .8 .8 45.0

2.44 2 1.7 1.7 46.7

2.46 1 .8 .8 47.5

2.52 1 .8 .8 48.3

2.55 1 .8 .8 49.2

3.05 1 .8 .8 50.0

3.10 1 .8 .8 50.8

3.12 2 1.7 1.7 52.5

3.16 2 1.7 1.7 54.2

3.19 1 .8 .8 55.0

3.23 1 .8 .8 55.8

3.26 1 .8 .8 56.7

3.27 1 .8 .8 57.5

3.30 1 .8 .8 58.3

3.37 1 .8 .8 59.2

3.38 1 .8 .8 60.0

3.42 1 .8 .8 60.8

3.46 1 .8 .8 61.7

3.47 1 .8 .8 62.5

3.52 1 .8 .8 63.3

4.09 1 .8 .8 64.2

4.10 1 .8 .8 65.0

4.12 1 .8 .8 65.8

4.14 1 .8 .8 66.7

4.19 1 .8 .8 67.5

4.29 1 .8 .8 68.3

4.35 1 .8 .8 69.2

4.36 1 .8 .8 70.0

4.38 1 .8 .8 70.8

4.41 1 .8 .8 71.7

29

5.01 1 .8 .8 72.5

5.03 1 .8 .8 73.3

5.04 1 .8 .8 74.2

5.13 1 .8 .8 75.0

5.20 1 .8 .8 75.8

5.26 1 .8 .8 76.7

5.31 1 .8 .8 77.5

5.35 1 .8 .8 78.3

5.48 1 .8 .8 79.2

6.05 1 .8 .8 80.0

6.17 1 .8 .8 80.8

6.21 1 .8 .8 81.7

6.22 1 .8 .8 82.5

6.23 1 .8 .8 83.3

6.32 1 .8 .8 84.2

6.34 1 .8 .8 85.0

6.53 1 .8 .8 85.8

7.05 1 .8 .8 86.7

7.13 1 .8 .8 87.5

7.14 1 .8 .8 88.3

7.20 1 .8 .8 89.2

7.34 1 .8 .8 90.0

7.50 1 .8 .8 90.8

8.16 1 .8 .8 91.7

8.39 1 .8 .8 92.5

8.45 1 .8 .8 93.3

8.49 1 .8 .8 94.2

8.56 1 .8 .8 95.0

9.47 1 .8 .8 95.8

11.40 1 .8 .8 96.7

12.03 1 .8 .8 97.5

13.08 1 .8 .8 98.3

15.35 1 .8 .8 99.2

21.36 1 .8 .8 100.0

Total 120 100.0 100.0

30 Table 16 - LnTime frequency

LnTime

Frequency Percent Valid Percent

Cumulative Percent

Valid

-1.51 1 .8 .8 .8

-1.47 1 .8 .8 1.7

-1.27 1 .8 .8 2.5

-1.24 1 .8 .8 3.3

-1.20 1 .8 .8 4.2

-1.17 2 1.7 1.7 5.8

-1.05 1 .8 .8 6.7

-.94 1 .8 .8 7.5

-.92 1 .8 .8 8.3

-.87 1 .8 .8 9.2

-.76 1 .8 .8 10.0

-.73 1 .8 .8 10.8

-.65 1 .8 .8 11.7

-.63 2 1.7 1.7 13.3

-.58 1 .8 .8 14.2

-.54 1 .8 .8 15.0

-.16 1 .8 .8 15.8

.00 1 .8 .8 16.7

.03 1 .8 .8 17.5

.04 1 .8 .8 18.3

.06 2 1.7 1.7 20.0

.15 2 1.7 1.7 21.7

.17 3 2.5 2.5 24.2

.21 1 .8 .8 25.0

.25 1 .8 .8 25.8

.28 1 .8 .8 26.7

.30 1 .8 .8 27.5

.36 1 .8 .8 28.3

.36 1 .8 .8 29.2

.37 1 .8 .8 30.0

.38 1 .8 .8 30.8

.39 2 1.7 1.7 32.5

.43 2 1.7 1.7 34.2

.46 1 .8 .8 35.0

.70 1 .8 .8 35.8

.71 1 .8 .8 36.7

31

.73 1 .8 .8 37.5

.74 1 .8 .8 38.3

.79 1 .8 .8 39.2

.79 1 .8 .8 40.0

.81 1 .8 .8 40.8

.82 1 .8 .8 41.7

.83 1 .8 .8 42.5

.84 1 .8 .8 43.3

.88 1 .8 .8 44.2

.89 1 .8 .8 45.0

.89 2 1.7 1.7 46.7

.90 1 .8 .8 47.5

.92 1 .8 .8 48.3

.94 1 .8 .8 49.2

1.12 1 .8 .8 50.0

1.13 1 .8 .8 50.8

1.14 2 1.7 1.7 52.5

1.15 2 1.7 1.7 54.2

1.16 1 .8 .8 55.0

1.17 1 .8 .8 55.8

1.18 1 .8 .8 56.7

1.18 1 .8 .8 57.5

1.19 1 .8 .8 58.3

1.21 1 .8 .8 59.2

1.22 1 .8 .8 60.0

1.23 1 .8 .8 60.8

1.24 1 .8 .8 61.7

1.24 1 .8 .8 62.5

1.26 1 .8 .8 63.3

1.41 1 .8 .8 64.2

1.41 1 .8 .8 65.0

1.42 1 .8 .8 65.8

1.42 1 .8 .8 66.7

1.43 1 .8 .8 67.5

1.46 1 .8 .8 68.3

1.47 1 .8 .8 69.2

1.47 1 .8 .8 70.0

1.48 1 .8 .8 70.8

1.48 1 .8 .8 71.7

1.61 1 .8 .8 72.5

1.62 1 .8 .8 73.3

32

1.62 1 .8 .8 74.2

1.64 1 .8 .8 75.0

1.65 1 .8 .8 75.8

1.66 1 .8 .8 76.7

1.67 1 .8 .8 77.5

1.68 1 .8 .8 78.3

1.70 1 .8 .8 79.2

1.80 1 .8 .8 80.0

1.82 1 .8 .8 80.8

1.83 1 .8 .8 81.7

1.83 1 .8 .8 82.5

1.83 1 .8 .8 83.3

1.84 1 .8 .8 84.2

1.85 1 .8 .8 85.0

1.88 1 .8 .8 85.8

1.95 1 .8 .8 86.7

1.96 1 .8 .8 87.5

1.97 1 .8 .8 88.3

1.97 1 .8 .8 89.2

1.99 1 .8 .8 90.0

2.01 1 .8 .8 90.8

2.10 1 .8 .8 91.7

2.13 1 .8 .8 92.5

2.13 1 .8 .8 93.3

2.14 1 .8 .8 94.2

2.15 1 .8 .8 95.0

2.25 1 .8 .8 95.8

2.43 1 .8 .8 96.7

2.49 1 .8 .8 97.5

2.57 1 .8 .8 98.3

2.73 1 .8 .8 99.2

3.06 1 .8 .8 100.0

Total 120 100.0 100.0