Taking charge of your own music experience: streaming platforms and humanized technology: An approach for incorporating human emotions into music streaming platforms in order to improve customer engagement and recommendations

i

TAKING CHARGE OF YOUR OWN MUSIC EXPERIENCE:

STREAMING PLATFORMS AND HUMANIZED TECHNOLOGY

Madalena Maria de Noronha Lopes e Silva Marques

An approach for incorporating human emotions into music streaming platforms in order to improve customer

engagement and recommendations

Dissertation presented as partial requirement for obtaining

the Master’s degree in Information Management with a

specialization in Marketing Intelligence

i

ii NOVA Information Management School

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

TAKING CHARGE OF YOUR OWN MUSIC EXPERIENCE: STREAMING PLATFORMS AND HUMANIZED TECHNOLOGY

by

Madalena Maria de Noronha Lopes e Silva Marques

Dissertation presented as partial requirement for obtaining the master’s degree in Information Management, with a specialization in Marketing Intelligence.

Advisor: Professor Diego Costa Pinto

November 2022

iii

ACKNOWLEDGMENTS

First of all, I would like to express my deepest gratitude to my supervisor, professor Diego Costa Pinto.

Additionally, I would like to thank all the respondents who took the time to fill out the online survey questionnaire, as well as the wonderful feedback I received. I am grateful to all my friends and family who have always supported and encouraged me in my academic career. Lastly, I cannot thank Diogo enough. My sincere appreciation goes out to all of you for your support and patience. As far as what's to come goes, I'm excited and ready.

iv

ABSTRACT

In recent years, music streaming services have gained more and more visibility, opening room for music recommendations to play a key role on improving listeners’ satisfaction. Most studies focus on the use of algorithms to understand what listeners prefer without asking them directly. For a more meaningful experience, it would be appropriate to explore how users want their states of emotions to be understood. To fill this gap, this study investigates the possible effects of introducing a more humanized factor into Spotify which, using a chatbot paired with fundamental algorithms, could facilitate music recommendations. Experimental research methods based on behavioral and technical patterns have been carried out through a defined survey. The findings indicate that most people are open to the idea of adding a chatbot to Spotify's music recommendation platform, because it gives them a greater sense of control over their music experience, emotions and well-being. The study, however, falls short of what could be reflected due to a few Portuguese participants who have never interacted with Chatbots before.

KEYWORDS

Music Streaming Services, Spotify, Algorithms, User Experience, Personalization, Chatbot.

v

INDEX

1. Introduction ... 1

1.1. Background and problem justification ... 1

1.2. Problem statement ... 2

1.3. Study Objectives ... 2

1.4. Relevance... 3

1.5. Thesis structure ... 3

2. Literature review ... 4

2.1. The music streaming market ... 4

2.1.1. Spotify's Algorithm: Helpful or Harmful? ... 5

2.2. Digital consumer behavior ... 6

2.2.1. The Music Streaming Consumers ... 6

2.2.2. Using Music to support mental-health ... 7

2.2.3. Music streaming algortithms ... 8

2.3. The usage of a chatbot ... 9

2.3.1. Chatbot: A definition ... 9

2.3.2. Music Chatbots ... 10

2.3.3. Chatbots literature: opportunities and threats... 10

2.4. Conceptual framework ... 11

3. Methodology ... 14

3.1. Research question ... 14

3.2. Data collection ... 14

3.3. Survey questionnaire (Experimental conditions) .

3.3.1. Procedure and Data collection ... 14

3.3.2. Materials and Participants ... 15

3.3.3. Data analysis ... 16

4. Results and discussion ... 18

4.1. Survey results analysis ... 18

4.1.1. Sample characterization - Demographics ... 18

4.1.2. Spotify usage ... 19

4.1.3. Spotify Streaming and habits ... 20

4.1.4. Chatbot experience on Spotify ... 21

4.1.5. Music as a tool to express emotions ... 28

vi

4.1.6. To use or not to use Mary the Chatbot ... 29

5. Conclusions and implications ... 34

5.1. Theoretical implications ... 34

5.2. Practical implications ... 36

5.3. Limitations and future research ... 37

6. Bibliography ... 39

7. Appendix ... 44

7.1. Survey ... 44

8. Annexes ... 55

vii

LIST OF FIGURES

Figure 1 - Time spent listening to music each week (IFPI, 2021). ... 6

Figure 2 - Weekly music engagement (IFPI, 2021) ... 7

Figure 3 - Music's positive impact on wellbeing (IFPI, 2021) ... 8

Figure 4 - Chatbot History (Araujo, 2018) ... 9

Figure 5 - Conceptual Framework ... 11

Figure 6 - Listeners use of subscription audio streaming(IFPI, 2021) ... 15

Figure 7 – Data preprocessing workflow ... 16

Figure 8 - What´s your gender? ... 18

Figure 9 - How old are you? ... 19

Figure 10 - Have you ever used a chatbot before? ... 19

Figure 11 - Usually, why do you listen to music? ... 21

Figure 12 - A hypothetical conversation between the chatbot and the user. ... 22

Figure 13 - Picking a song usually takes a lot of effort. ... 23

Figure 14 - It is easy for me to find a song or playlist that goes along with my mood. ... ….23

Figure 15 - When Spotify suggests a song, I trust its recommendations. ... 24

Figure 16 - My emotions may be affected by the music that is suggested to me, so I need to gain control over its recommendations. ... 24

Figure 17 - It seems odd that the more I listen to Spotify r4ecommendations, the more I

Figure 18 - I want to branch out from my usual music routine and hear new music…………...33

Figure 19 - As seen above, using the Chatbot would be easy since there is no writing involved and the responses would be sent automatically. ... 26

Figure 20 - Using Mary, the Chatbot on Spotify would require effort and time………..…….34

Figure 21 - The Chatbot would enrich my experience with Spotify………...35

Figure 22 - I think having a Chatbot within Spotify could help me keep track of my everyday

Figure 23 - If there was a chatbot like Mary within Spotify, I would use it ... 29

Figure 24 - Participants who have never used a chatbot and unreceptive responses. ... 30

Figure 25 - Age distribution plot of unreceptive participants to the chatbot ... 30

Figure 26 - Instead of having this interaction as a permanent feature, I'd like to be able to

activate or deactivate the chatbot as needed ... 31

viii

LIST OF TABLES

Table 1 – Answers given to the questions: “Usually, why do you listen to music?” ………..25

Table 2 – Answers give to the question: “Think of the chatbot as a human being attempting to

recommend a song or playlist based on your current mood. Please rank which of the

following emotions you would like the chatbot to consider.” ………...……..37

Table 3 – Results analyses testing whether concrete (versus abstract) message frames would

impact (a) Well-being, (b) Brand trust, (c) Brand loyalty, and (d) Perceived quality…….43

Table 4

cues………43

1

1. INTRODUCTION

1.1. B

The use of recommendation systems has increased in the most diverse areas, through filtering systems that predict consumer tastes and trends (Sharma et al., 2021). Most companies use such systems to make it easier to know customers’ preferences and suggest a similar set of products (Sharma et al., 2021). Customers should be able to find the product they need within a short time, and companies should offer customized products to meet their individual needs. A common recommendation system practice is providing suggestions of songs via music streaming services (Yousefian Jazi et al., 2021b).

While music is constant in our daily lives, the notion of recommendation systems is closely tied to this idea, as each person's musical taste tells us a lot about the individual. According to (Ayata et al., 2018),

"considering emotion state of the listener improves the performance of recommendations”, highlighting the challenge of recommendation systems meeting customer needs (Prey, 2018).

Studies show that most recommendations account for the mood of the listener because it is a factor that assists the algorithms upon which they rely. These sets of songs offer selections that are likely to meet the listener's needs, which consequently sets their taste patterns and subsequent musical choices (Ayata et al., 2018; Song et al., 2012; Yousefian Jazi et al., 2021a; Zhou et al., 2018). Spotify is one of the world's largest premium streaming services, with thousands of choices and trustworthy users that rely on its generated playlists (Fry, n.d.). By suggesting songs which listeners are familiar with, Spotify's BART algorithm keeps its customers engaged, encouraging a balanced relationship between human and machine input (Fry, n.d.).

According to (Gillespie, 2014), recommender systems should go beyond algorithms, mainly because there is a concern that they might encourage people to focus on too narrow sets of content and to limit themselves to what they already know (A. Anderson et al., 2020). Trying to understand the social power of algorithms requires understanding how notions are conveyed and what they can achieve (Beer, 2016). As of yet, no subjective music recommendation system has been fully investigated, meaning that most algorithms attempt to understand patterns indirectly, rather than asking users directly what they would like to listen to (Song et al., 2012).

As stated in (Yousefian Jazi et al., 2021b) , “User’s emotions can be identified explicitly or implicitly.

Explicit methods use direct inquiry from the user”. This is particularly useful because it opens the possibility of describing a listener's momentary emotional state and suggesting exactly the music they want, avoiding bias based on previously listened music (Kowald et al., 2020). Benefiting from the existing algorithms and adding a human eye factor means laying down a clear path for an application with both (Kamehkhosh et al., 2020; Song et al., 2012).

There is also a fast-paced evolution of interactions between brands and individuals, in such a way that companies are now faced with the challenges of conversational commerce, which can enhance customer service in a new era fueled by natural language technologies (Rita & Quintino, n.d.). Chatbots in social media and messaging apps are becoming more popular as a form of communication for consumers and the fastest growing channel for brand conversations within these platforms.

2

1.2. P

As a brief summary, the music industry is moving towards streaming services such as Spotify, which analyze patterns and suggest content based on those patterns (Helmholz et al., 2020). It is evident that explicit consideration is necessary when generating algorithmic recommendations for users, as well as the need to efficiently balance them (Mehrotra, 2021). Researchers investigated how human personality is related to the ways in which people listen to music on Spotify, and at least two possible mechanisms were identified: people may search for music that reflects their personalities while, at the same time, their personalities may be shaped by the music they listen to and are exposed to (I.

Anderson et al., 2021).

These platforms play a significant role in shaping music consumption at scale, as well as listener behavior, although its significance has not yet been fully explored and adapted to the new user conditions. In this case, consumers' emotions are strongly influenced by their listening behaviors, which is why we must also consider their emotions when discussing this topic. At present, providers rely mostly on predefined playlists for different moods or situations when selecting emotional music. It is also common for users to feel overwhelmed when choosing music (Helmholz et al., 2020).

Yet, further research is needed to understand how musical preferences and personality develop throughout life and how they interact, especially during childhood and adolescence, when social pressures and identity formation are prominent. To better understand emotions, we are proposing a chatbot that could be integrated within any music streaming platform to implement a more humanized approach. These results are hoped to provide substantial improvements in music recommendation quality on one of the world's largest music streaming platforms, Spotify, and, therefore, in helping people choose the right fit for their moods.

1.3. S

O

The purpose of this study is to discover how a chatbot can be used by Spotify to improve customer service by answering the following research question:

Can music streaming platforms (e.g., Spotify) use emotional content algorithms to improve customer participation and song recommendations?

To contribute to the performance of the research, an additional set of sub-questions is defined:

• How would music listeners react to the idea of navigating their own musical journey?

• To what extent does Spotify consider listeners' real musical tastes when making personalized recommendations?

• What sort of experience would Spotify listeners have when communicating with a humanized Chatbot who asks about momentary emotions?

It is essential to understand that, even in this increasingly technological world, humans may still desire some control. A song recommendation should reflect the user's emotions and the experience they point to. When using Spotify, it is important to understand how the existing algorithms can be balanced with the person’s own emotions, habits, and tastes. Essentially, this study aims to determine whether a platform like Spotify could recommend music more accurately if it gauged a user's emotional state.

3

1.4. R

Music affects consumers emotionally, and this can play a vital role in our lives. Music is, after all, a medium that alters our emotions and elevates us to have higher feelings and experiences (Li et al., 2021). Thus, advances in digital technology have allowed music to be at people’s fingertips, providing them with direct access to more music than ever before through venues such as Spotify (I. Anderson et al., 2021). Moreover, as stated by Statista, Spotify had 188 million premium subscribers worldwide by the second quarter of 2022, an increase from 165 million in the same quarter in 2021 (Marie Charlotte Götting, 2022b). Moreover, Spotify's subscriber base has doubled since early 2017 and has more than doubled in the last few years (Marie Charlotte Götting, 2022b). This demonstrates how music is crucial to the current market of moods of each user, and how Spotify's socio-material assembly also has a critical role in creating such a market (Siles et al., 2019).

Additionally, messaging apps have surpassed social media apps in active users (Statista Research Department, 2015). This would enable Spotify to reach their customers more effectively, for example, through chatbots. Chatbots, however, pose risks since they can frustrate users if they aren't designed based on their needs (Jiang et al., 2022). Nevertheless, considering the impact of chatbot attributes on customer experiences, in an app such as Spotify it could possibly help fill research gaps regarding the importance of taking emotions into account.

1.5. T

The next chapter presents a literature review, followed by the research methodology, its results, findings and conclusions. The literature review explains four essential points: the current music streaming market reality; the importance of the digital consumer experience; chatbot technology and the nature of its interactions; and finally, a framework that provides further insight to understand the topics at hand. The third chapter presents the methodology used to answer the research questions, and the approach used for survey design. The fourth chapter consists of the analysis of the results, followed by the fifth and final chapter, which contains the conclusions, the limitations of the dissertation, and suggestions for future research on the topic.

4

2. LITERATURE REVIEW

2.1. T

Music streaming services have been one of the fastest growing industries in recent years due to digitalization (Yong Chai et al., 2021). As the recording music industry entered the digital age, piracy and illegal downloading caused significant turmoil, as well as royalty issues for artists via streaming services. Until now, most of the research examining this shift has focused on consumer decisions, such as whether to download music illegally, and, more recently, on streaming experiences (Green &

Sinclair, 2022).

The popularity of music streaming services has led to the challenge of keeping customers engaged in the musical experience (Prey, 2018). In a quick search of Scopus, we discovered 612 publications that included the phrase "Music Streaming Services" within the title, abstract, and keywords, out of which 521 were published over the past five years, suggesting the topic is becoming important. Although there are increasing numbers of studies covering this topic, several gaps remain (Appendix A). Thus, to fully understand a music recommender, we must comprehend the unique character of each user (Song et al., 2012).

There is a common goal in all these studies: to decipher the listener's emotions, either explicitly requested or implicitly obtained (Schedl et al., 2018). Studies have explored several methods of determining what the listener might want and tackling issues regarding how music recommendations affect daily life (Lee et al., 2019). For instance, a study has developed a model called GEMS, to determine musical emotion (Zentner et al., 2008). In related research, a location-based music recommendation system was proposed (Cheng & Shen, 2014), as well as a social media recommendation system based on mood data (Wishwanath & Ahangama, 2019). Also, several proposals use collaborative filtering (Sánchez-Moreno et al., 2020), as well as others that adapt to changes in the temporal context called TARS (Sánchez-Moreno et al., 2020). A recent study examined a music recommendation system based on customers' keystrokes and click patterns (Yousefian Jazi et al., 2021a). Another approach involved developing a chatbot to assess how the user feels. By asking a few general questions, it determines the user’s emotions, and based on that information, a score for each response is calculated, adding up to the final score used to create the playlist (Nair et al., 2021).

Nevertheless, according to the resources cited, future research should also focus on humanizing these algorithms from the listener's perspective to increase customer engagement and to respond appropriately to user input (Lee et al., 2019; Nair et al., 2021). This study aims to establish a relationship between emotions and algorithms in Spotify so that users feel more in control of the music that is recommended to them.

5 2.1.1. Spotify's Algorithm: Helpful or Harmful?

In recent years, recommendation engines have become indispensable in art, culture platforms and services. Major players in this field, including Spotify (music) and Netflix (movies and TV shows), rely heavily on their recommendation engines. Even though these recommendation engines vary in the context in which they are used, their general functioning is somewhat similar, since they are designed with one goal: to encourage users to stay on a particular platform or website, by constantly offering them relevant or enjoyable content (Knijnenburg et al., 2016).

Music streaming platforms have changed how we listen to music, and as people get busier, these platforms have become more viable alternatives. A major player in the music streaming industry is the Sweden-based company Spotify (Hamdani & Permana, 2021). As per Statista, Spotify has dominated the music streaming market in recent years, leaving competitors like Apple Music and Amazon behind, and since its founding it has provided a platform for a wide variety of music content, as well as others (Marie Charlotte Götting, 2022a). Spotify reported 232 million monthly active users in July 2019, including 108 million paying subscribers (Marie Charlotte Götting, 2022b). While many listeners see Spotify as a window into great music collections, it is an intricate algorithm-driven network of music recommendations (Werner, 2020).

Algorithmic culture is highly influenced by streaming services, a culture in which values are determined partly by humans and partly by machines. Spotify's recommendations cannot be fully understood because the software is learning from experience and building complex systems that function like black boxes (Mackenzie, 2017). In other words, search engines and recommendations heavily influence content discovery withing streaming platforms.

According to Striphas (2015), algorithmic culture hides the way corporations shape cultural values and social groups through their algorithms, and although Spotify may aim to personalize their music experience and guide users to songs they'll love, the result may be more than coincidental or innocent, but rather to further reinforce power structures.

As part of a study conducted by (Jeremy Wade Morris, 2020), listener bots such as Spotify's “SpotiBot”

(automated scripts that resemble users) were used to provide pieces of their own music on Spotify, map how algorithms feed Spotify Radio looped music and examine how algorithms rank the age and gender of users. “SpotiBot's” results support the findings of (Kaitajärvi-Tiekso, 2021): inside the black box of streaming music, a radio stream is generated based on a song chosen by the listener, then the song is repeated frequently and, probably, even more often if it is a hit.

As a result of using recommendation engines, users may be presented with fewer diverse content options, as they are mostly offered content, they are already interested in. This would allow them to only see information relevant to their interests without being exposed to other information (HesterJuliaVoddé, 2021). It is therefore imperative to study and continually seek more humanistic solutions to the potential dangers posed by recommendation engines.

6

2.2. D

Consumption is an integral part of our daily lives and is influenced by a variety of factors, including our emotions, social motivations, and cognitive abilities. The power of search lies in its ability to put power in the hands of users, especially if it is intuitive, effective, and easy to use. Thus, it is crucial to understand how users use this system to design the best search platform for them (Hosey et al., 2019).

In a study made by (Hosey et al., 2019a), semi structured interviews were conducted to answer several questions, one of which was "How did participants search on Spotify?". Although participants sometimes did not find the exact content they were looking for, the search on Spotify was viewed as an important way to navigate to music due to its high level of control. It was generally the case that participants tried different techniques until they found the information they were seeking, but if they felt they had exhausted all the strategies they knew, they let the search end. This study concluded that search is a dynamic process involving user changes, rather than a static process. Furthermore, it revealed the importance of assessing how to operationalize from the user's perspective and the importance of inferring their mindset.

2.2.1. The Music Streaming Consumers

Based on a survey of 43,000 music fans across 21 countries - the largest study of this kind in 2021 - IFPI found that fans not only listen to more music, but they are also taking advantage of new, dynamic, and immersive musical experiences, considering that the amount of time people spend listening to music each week has increased from 18 hours since 2019 (IFPI releases Engaging with Music 2021, 2021).

Figure 1 - Time spent listening to music each week (IFPI, 2021).

7 Also, 23% of those surveyed listen to music through subscription streaming services (e.g., Spotify Premium, Apple Music, Melon), the largest percentage of all.

2.2.2. Using Music to support mental-health

The impact of music on everyday emotions is highly valuable, valid, and reliable and almost all emotion structures in the brain become activated when music evokes intense emotions. There is a strong therapeutic significance in this because it suggests that music can be used in the treatment of disorders and diseases with emotional components, or dysfunctions in limbic/paralimbic brain structures (Koelsch, 2018).

Thus, throughout cultures worldwide, music plays a vital role in creating entertainment, establishing identity, expressing feelings and emotions, and delivering a shared, as well as a highly individual experience. 87% of respondents to the survey conducted by the IFPI said that music brought them pleasure and happiness throughout the pandemic (IFPI, 2021), and that in the challenging times of Covid-19, music was proven to be a source of comfort and healing for listeners. Thus, it plays an

Figure 2 - Weekly music engagement (IFPI, 2021)

8 important role in enhancing emotional well-being, something which fans recognize. It is apparent that people around the world call on music for comfort, pleasure, and escapism in everyday activities, which has a positive impact on their mental health.

2.2.3. Music streaming algorithms

There is a lot of music available on music-streaming platforms, so finding the right music can be challenging. Users are often required to search through these platforms' extensive catalogs (Hosey et al., 2019a). Considering this availability of substantial online collections of music on streaming platforms like Spotify, people are increasingly reliant on algorithms that recommend music and automate curation and discovery (Freeman et al., 2022). As a result of daily interaction with algorithmic features and curations, listeners are increasingly forming socio-technical relationships with these algorithms, involving human-like factors such as trust, betrayal and intimacy. In this way, music recommendations play a role in influencing users' tastes and music listening habits (Freeman et al., 2022).

(Panda & Paiva, 2021) argues Spotify mitigates these issues with a variety of data-driven personalization methods and manually curated playlists, most of them based on user listening history.

Thus, traditional recommendations rely on collaborative filtering (C. Johnson, 2014), techniques that depend on users' consumption patterns.

Even though recommendations are highly effective, there is always room for improvement, since they are not fully predictive (Sherga Jr et al., 2021). In a study conducted by (Mehrotra, 2021), two user- centric recommendation products were subjected to a series of live A/B tests on a large population of users to evaluate their effectiveness on key engagement metrics. It was found that explicit

Figure 3 - Music's positive impact on wellbeing (IFPI, 2021)

9 consideration and optimization of such objectives is necessary, as well as balancing them efficiently when generating algorithmic recommendations. It is thus clear that music platform recommendation should be designed to meet users' varied needs, and not just the ones they are familiar with.

Despite this, Spotify has been exploring ways to display listener activity information. This is evident in Spotify's summary of listeners' favorites for the year: artists, songs, and genres, plus mood boards in one-click interactive stories and the option to "mix" songs with friends. In this way, personal information from a user's listening history enables them to "reflect upon their identities as listeners"

(Charlotte Hu, 2021).

2.3. T

2.3.1. Chatbot: A definition

Conversational agents or chatbots are programs that can respond to natural language inputs and make attempts to mimic human conversations (Reshmi & Balakrishnan, 2016) using audio (e.g. Siri from Apple) or textual inputs (Winkler & Soellner, 2018). Throughout this dissertation, text-based chatbots will be the focus.

As per recent definitions, chatbots are automated systems that respond to human-like language text as well as execute specific commands, in which structured messages, images, links, or specific call-to- action buttons are typically used in instant responses (Zarouali et al., 2018), which does not allow for an embodied, real-time, dynamic representation of the interaction. Companies provide information and advice to customers, or customers initiate the interaction themselves. As a result of their individual questions, they receive real-time information (van Dolen et al., 2007).

Figure 4 - Chatbot History (Araujo, 2018)

10 2.3.2. Music Chatbots

There has been a recent study that indicates that 16 young people perceive social chatbots differently over time, particularly when it comes to the types of social support they experience daily. In the research, it was discovered that chatbots are effective as evaluative, informative, emotional and instrumental sources of support, primarily because they are perceived as providing a safe, anonymous space for conversation and confession because of their low threshold nature (Brandtzaeg et al., 2021).

“Brand anthropomorphism” is a powerful marketing strategy for connecting with customers, since it involves endowing brand symbols with human characteristics (Salles et al., 2020). According to (Araujo’s, 2018) findings, it was also found that chatbots with human-like cues enhance users' emotional connection with the company, indicating that "humanized chatbots” can enhance relationship building. Thus, analyzing human-like cues demonstrated that, by increasing the anthropomorphic features of an interface, people feel more understood, and the website has more utility (Dugan et al., 2011).

With AI's ability to learn and iterate, music has seen some significant developments. In the case of Spotify, the company utilized technology, particularly big data, to provide a personalized experience for each user and is using data to train machines and algorithms (Priya Dialani, 2021). With chatbots, listening to music should be facilitated in a way in which tracks are chosen according to the mood of the listener. In addition to improving music recommendation, these "humanized" chatbots give users the feeling of being heard and more in control (Borawake et al., 2022).

2.3.3. Chatbots literature: opportunities and threats

Social media chatbots are becoming more popular, but despite their growing popularity, these virtual agents still require a careful implementation to avoid unintended outcomes. In marketing research studies, researchers have found that the presence of basic social cues can influence customer satisfaction, enjoyment and purchase intentions (Köhler et al., 2011).

(Nowak & Rauh, 2005) however, believe that anthropomorphic virtual agents may create higher expectations that are more difficult to meet. This is because they tend to rely on complex technological features, especially when examining emotions. According to a study by (Hume et al., 2022), which examines the role of personality in social interaction, questions/help requests were asked and answered using the "Big-five" traits (Extraversion, Agreeableness, Conscientiousness, Neuroticism and Openness to Experience). This was helpful when looking at emotions and personality traits. Whenever customers' expectations are not met, they feel disappointed and tend to reduce their perceptions and evaluations (e.g., credibility, likeability). On this basis, (ben Mimoun et al., 2017) argues that agents fail due to the negative gap between consumers' expectations and their performance. Thus, its functionality and aspect should be in balance to meet customers' expectations and, when designing a chatbot, equilibrium is crucial.

11 Therefore, chatbot-related literature is still expanding in terms of customer satisfaction, attitudes towards a company or brand, emotional attachment, and likelihood of recommending a chatbot (Araujo, 2018). One of the purposes of this dissertation is to fill this research gap by investigating what effect chatbots can have on customers' music experiences. In the following chapter, we discuss the methodology regarding these topics.

2.4. C

Using a chatbot, this study examines how users and algorithms on Spotify can interact to identify momentary emotions and recommend music based on those emotions. As a major objective of this dissertation, we will examine how a chatbot's prior engagement with human-like cues, as well as its awareness of users' emotions (independent variable), can lead to personalized music recommendations (dependent variable) and ultimately improve customer satisfaction, as well as aspects such as their well-being within Spotify, brand trust, loyalty and perceived quality of the platform.

Hypothesis

Accordingly, most research on recommendation systems in music focuses on prediction and optimizing ranking. In recent years, however, researchers have demonstrated that other aspects of recommendations, such as transparency, control, and overall user experience, are crucial for a positive user experience (Andjelkovic et al., 2016). It has been reported that even more nuanced guidelines

Figure 5 - Conceptual Framework

12 need to be given to provide authentic engagement between brands, products and users, so that it becomes even more personal (Eigenraam et al., 2021).

It is predicted in this thesis that humanized interfaces will lead to increased streaming engagement because these interfaces put the human as a free individual who can make decisions involving his own personal choices into perspective. It is possible to bridge the current gap between the importance given to the evaluation of recommendation algorithms and the range of ongoing research efforts by focusing on human decision making and humanizing it even more. Thus, this demonstrates the importance of using recommendation algorithms that are aware of human decision-making, which will probably benefit them (Chen et al., 2013).

H1. Humanized (vs. not humanized) interfaces improve streaming engagement.

H1a. Humanized (vs. not humanized) interfaces improve streaming engagement by positively improving costumers’ well-being, brand trust, brand loyalty, and perceived quality of Spotify.

H2. Personalized (vs not personalized) streaming leads to higher engagement.

H2a. Personalized (vs not personalized) streaming leads to higher engagement by positively improving costumers’ well-being, brand trust, brand loyalty, and perceived quality of Spotify.

A similar point is that music lends itself to a highly personal approach, which is why it is especially interesting to investigate in the context of personalization (Prey, 2018). As a result, a personalized experience is more likely to boost user satisfaction and engagement.

It has also been discussed above that consumers expect companies such as Spotify to deliver on their guarantees regarding the best music recommendations and their music experience. Although Spotify's recommendations are highly effective, there is always room for improvement since they are not fully predictive (Sherga Jr et al., 2021). Considering the literature studied, it became apparent that Spotify recommendations should be designed to meet the diverse needs of users, rather than just those they are familiar with, as well as the importance of evaluating how to operationalize from a user's perspective and inferring their mindset on Spotify (Hosey et al., 2019b). These previous literatures have led to the presented hypotheses aiming to clarify the proposed research question and problem statement. This set of facts may be explained by a relationship between the two variables, so that these hypotheses may be further tested (Mourougan & Sethuraman, 2017).

13 Variable descriptions

To test hypothesis 1 and 2, we selected five dependent variables, namely:

• By asking participants whether their emotions might be affected by the music that was suggested to them, well-being was evaluated (1- Strongly disagree, 5- Strongly agree).

• In order to assess brand trust, participants were asked whether they trust Spotify's recommendations (1- Strongly disagree, 5- Strongly Agree).

• In order to gauge brand loyalty, we asked participants if it seems odd that, the more they listened to Spotify recommendations, the more they contributed to an algorithm that generated more similar recommendations (1- Strongly disagree, 5- Strongly Agree).

• To determine perceived quality, participants were asked whether picking a song takes a lot of effort (1- Strongly disagree, 5- Strongly Agree).

• To determine whether chatbots display human-like cues, participants were asked if they thought Spotify's Chatbot could help them keep track of their emotions on a daily basis (1- Strongly disagree, 5- Strongly Agree).

To prove the study's validity, we selected two independent variables:

• To assess search on Spotify, they were asked whether they wanted to hear new music outside of their usual music routine. (1- Strongly disagree, 5- Strongly Agree)

• To assess Spotify search based on moods, participants were asked whether it is easy for them to find a song or playlist that matches their mood. (1- Strongly disagree, 5- Strongly Agree) In the formulation, two questions were interconnected and could not be separated due to their impact.

As a result, both questions were treated as independent variables. In order to ensure that the effects of one independent variable depend on the level of another, we performed several tests comparing the R square score of one independent variable versus both variables. We obtained better results when we used both independent variables. Consequently, a positive effect was demonstrated.

14

3. METHODOLOGY

The purpose of this study was to examine how a chatbot could be used to enhance interactions between users and algorithms in Spotify by identifying momentary emotions and recommending music. The main objective of this chapter is to present the research method used and to provide a detailed description of the variables used in the study to answer the stated research questions.

Through quantitative research methods, platforms can maximize their usefulness while focusing on humanization. This technique is a good way to overcome these current limitations and analyze results, because it allows for generalizations based on a larger sample size, which is useful when quantifying behavior and attitudes (Almeida et al., 2017). Thus, findings can be used to demonstrate patterns between users, enabling them to determine its purpose and question how a more humanized approach in music streaming services can be considered.

3.1. R

This study's focus is determined by interpreting its objectives and substantiating them with evidence.

Research objectives can be determined based on the literature review, as it helps identify gaps, defines consistent problems, and identifies research objectives. To begin, the best course of action is to define a specific research question, mentioned earlier: Can music streaming users (e.g., Spotify) use emotional content algorithms to improve participation and song recommendations?

3.2. D

This paper draws on multiple sources of data, including an extensive literature search in Scopus and Mendeley, the primary sources of data collection. These searches range from broad concepts like recommendations and algorithms in multiple areas, to a more detailed analysis of these platforms' human perceptions and tastes.

3.3. S

(E

)

Survey Questionnaire

In this study, a link provided by Qualtrics allowed participants to answer questions related to their interactions with a Spotify chatbot (in this case, Mary). Participants were asked to respond as if they were dealing with a chatbot, such as Mary, in a hypothetical situation.

15 3.3.2. Materials and Participants

Based on a survey of 43,000 music fans across 21 countries - the largest study of this kind in 2021 - IFPI found that listeners’ use of subscription audio streaming was highest in younger demographics*, something taken into consideration when choosing our participants age. (IFPI, 2021).

Based on this, this survey will ask Spotify users over the age of 18 a set of questions in a format that has been carefully evaluated. Qualtrics, an experience management platform, will be used for the questionnaire, which will consist of a series of questions. There will be questions about each user's reactions to the possibility of a Chatbot on Spotify (Mary), asking them about their emotions, thus allowing a deeper analysis of the results and identification of patterns (Hainmueller, 2014; Schedl et al., 2018).

Next, all participants responded to a series of multi-item measures that were organized in five sections:

1. Agreement / Informed consent form 2. General participant information

3. The chatbot baseline to familiarize participants with it

4. "Please rate the following statements" based on 5 essential aspects for the study:

• Listening habits

• Recommendations

Figure 6 – Listeners’ use of subscription audio streaming(IFPI, 2021)

16

• Algorithms

• Relationship with the chatbot

• Experience with Spotify

5. Two more general questions about Mary's approach within Spotify and emotion considerations for users.

3.3.3. Data analysis

Data analytics can be used to settle on findings based on unrefined data. Thus, finding patterns and measurements can be analyzed to improve procedures for expanding the research question.

Data preprocessing

As part of this project, various techniques were applied to the data from the completed form to create the final dataset. Data processing was the first step to ensure that the answers are from respondents who have completed the entire survey, and that each question was formatted correctly. Secondly, the data was cleaned, i.e., invalid responses were deleted and people who have never listened to Spotify were removed (outliers). The third step was to characterize the sample using Frequency and Descriptive Statistics, examining topics like demographics, Spotify usage habits, reasons for listening to music, interest, and adoption of new technologies like chatbots and, finally, chatbot experiences based on individual emotions.

In developing these processes, Python language was used and preferred over other tools, mainly due to its wide range of libraries. Python includes an extensive database library containing artificial intelligence and artificial knowledge, such as Scikit Learn, Tensorflow, Pytorch, Pandas and Matplotlib for operational analytics and data science. Nevertheless, the following libraries were used in this project:

Figure 7 – Data preprocessing workflow

17

· Pandas: It offers a variety of features and commands for easy data analysis, along with the ability to segment and segregate data according to preferences or filter it according to specific criteria (Pandas, 2022). For this project, the Pandas library was used to perform data cleaning processes, to change question labels to the correct format, and to remove answers that did not meet the analysis criteria.

Additionally, the library helped to sub-select each question from the tabular data.

·NKLT: An NLP library that works with human language data, providing libraries for tokenization, parsing, classification, stemming, tagging, and semantic reasoning (NLTK, 2022). NKTL library was used to tokenize sentences, identify key words, and count the most frequently mentioned words based on respondents' opinions.

· Plotly: An open-source plotting library that supports over 40 chart types covering a wide range of statistical topics (Ploty, 2022). The Plotly library was used to visualize histograms, Word Clouds, and pie charts generated during the preprocessing. Each question was statistically fully analyzed using this package.

Hypothesis testing – Test of Significance

A significant test is used in this study as one of the methods for analyzing whether the data supports or refutes the claims. For this case, we use regression analysis to determine whether the independent variable explains the dependent variable's variance.

18

4. RESULTS AND DISCUSSION

Through a survey, this study explored the possibilities and design space inside Spotify for a chatbot collecting explicit feedback about users' moods. It was examined if a chatbot that analyzes user emotions and recommends music based on those emotions could facilitate a more meaningful experience for users.

4.1. S

4.1.1. Sample characterization - Demographics

As part of the initial sample, a total of 312 people was chosen, but only 200 of them had ever used Spotify. As a result, 200 individuals were analyzed in the sample for this study, with the majority being female (61.5%), which is in line with (Every Noise at once, 2022) Spotify statistic of 47.7% female streams. Despite how randomly we framed this questionnaire to different people, there may always be biases associated with it, since these values represent a sample of a larger group, not an average gender distribution of listeners.

Figure 9 below illustrates the age distribution of respondents, which shows that the majority (30.5%) had ages between 25-34, followed by 18-24 (29.5%). Overall, these two segments account for 60.0%

of the sample, with 45-54-year-olds representing the highest percentage (20.5%), followed by all other segments with 19.5%.

Figure 8 - What´s your gender?

19 4.1.2. Spotify usage

As part of this analysis, we sought to identify which percentage of the sample had already used a chatbot. Only 41.0% of the people in this sample had ever used a chatbot, which is quite interesting since the majority of them have never used one.

To conduct this analysis, we sought to determine how many of the sample members had already used a chatbot. According to the information above, only 41.0% of people in this sample have ever interacted with a chatbot. Also, according to (Eurostat, 2021) only 9% of Portuguese companies use intelligent systems like a chatbot to help the final consumer. A lack of intelligent systems in Portugal may contribute to the high number of people who have never used or interacted with chatbots in this sample.

Figure 9 - How old are you?

Figure 10 - Have you ever used a chatbot before?

20 4.1.3. Spotify Streaming and habits

Motivations to listen to music

In addition, the study examined how contemporary music listening is intertwined with other daily practices in everyday life, to gain an understanding of what motivates people to listen and consume music in the digital age. 36% of participants reported listening to music to relax, which is important information about people's well-being, one of the key points of this study. Secondly, 25% of the participants listen to music as they work, the so-called soundtrack. In addition, the results presented in the table below indicate that listening to music is most commonly associated with other daily activities.

Feeling Percentage

relax

work 25%

music 20%

love 16%

feel 10%

fun 10%

good 9%

relaxing 8%

mood 7%

focus 6%

Table 1 - Answers given to the questions: “Usually, why do you listen to music?”

Most of the data for this question was unstructured and readable by humans. For this reason, we used a Natural Language Toolkit from a Python library (NLTK, 2022) to determine the top ten key feelings.

This allowed us to calculate the top ten feelings using natural language processing (NLP). The results of the survey are displayed in the following Word Cloud.

21 4.1.4. Chatbot experience on Spotify

The starting point was sharing a hypothetical situation when using Spotify, followed by statements describing the sample's perception of this chatbot. Next, a chatbot was introduced to Spotify to evaluate whether it would be viable and to understand participants' difficulties when choosing songs and their momentary emotional sharing behaviors.

Hypothetical situation

Since chatbots were not designed to test participant-chatbot relationships, we asked survey participants to imagine a situation in which they would encounter a Chatbot on Spotify. Their first task was to choose a song on Spotify based on a fictional scenario. Moreover, we wanted to attribute a feeling they might be experiencing, in this case excitement. Also, as they selected a song, a Humanized Chatbot welcomed them: "Hi, I am Mary the music chatbot! How are you feeling today?". They were explained that this would be a very detailed conversation with highly empathic answers to their questions. Their first impression would be that the chatbot was genuinely committed to understanding their emotions and choosing the right song for them.

Figure 11 - Usually, why do you listen to music?

22 Listening Habits

According to previous discussions, streaming music has become the most current method of listening to music, and the popularity of this technology keeps on growing, enabling people to access an extensive catalog of music, which sometimes makes it even more difficult for them to choose the right one. To understand user experience on Spotify, we first examined how users do their search, as well as what makes a good or bad experience from their perspective.

Figure 12 - A hypothetical conversation between the chatbot and the user.

23 In figure 13 - Picking a song takes a lot of effort - we can see, the distribution skews to the right in terms of difficulty when choosing music, with a median choice of “neither disagree nor agree” (3,0) and a mode of “somewhat agree” (4,0). In general, 83 of respondents agree that choosing music is difficult, while the other half disagree or don't have an opinion. Looking at figure 14 - It is easy for me to find a song or playlist that goes along with my mood -, about 41% (82 people) agree that it is easy to choose a song for a particular mood. The results show that less than half of the participants have trouble choosing the right song for their mood. In this figure, data distribution is skewed to the right with mode (4,0) and mean of somewhat agree (3,7). The data sample on the first figure indicates that most people take a lot of effort to pick a song, although there are many people who may disagree with this (78 respondents). This may explain how unaware they are of their momentary emotions and, thus, how difficult it is for them to find a song that appeals to them at the time. From figure 14, we can conclude that most people find it easy to choose songs that fit their mood. Thus, they may be able to make their decision more easily when aware of their mood.

Figure 14 - It is easy for me to find a song or playlist that goes along with my mood.

Figure 13 - Picking a song usually takes a lot of effort.

24 Recommendations

Seamlessly accessing content is a major focus of Spotify's mission to provide users with effective, personalized, and interactive experiences. Consequently, in this part of the analysis, we explored how users may perceive the recommendation process and feel satisfied on how it impacts their mood.

As it was mentioned before, Spotify keeps recommending new music to constantly find innovative ways to analyze, compare, contrast, sort and group information collected from its users. This is reflected in figure 15 - When Spotify suggests a song I trust its recommendations - regarding Spotify's algorithm for suggesting songs. In the survey, almost 50% of participants said they trust Spotify's suggestions, while 49 people answered neither agree nor disagree, with the rest choosing “strongly agreed” or “disagreed”. As it shows, algorithms still hold value for people and should still be taken into consideration. Based on a mean between “Neither agree nor disagree” and “Somewhat agree” (3,4), and a mode of “Somewhat agree” (4,0), a right skewed distribution was obtained.

Furthermore, when considering figure 16 - My emotions may be affected by the music that is suggested to me, so I need to gain control over its recommendations - we can see that many respondents agree that they feel they must control the recommendations in accordance with their moods. As opposed to the previous question, this one appears to have a more normal distribution, with 31 people strongly agreeing and 67 agreeing, and a further 53 people disagreeing with this statement (20 strongly disagree and 33 disagree). Additionally, the remaining people were neutral, the same result as the previous question. There is a mean value of “Neither agree nor disagree” (3,3) and a mode value of

“Somewhat agree” (4,0).

Figure 15 - When Spotify suggests a song, I trust its recommendations.

Figure 16 - My emotions may be affected by the music that is suggested to me, so I need to gain

control over its recommendations.

25 The results by both graphs in terms of recommendations reflect that most people like recommendations. They do, however, feel the need to control its suggestions when we talk about their mood, perhaps because of their mental state since, as we noted before in our literature review, music has a strong influence on the mind.

Algorithms

With Spotify's personalized recommendation system, users are becoming more familiar with its complex algorithm. This project arose from a question about how effective recommendation algorithms were - To what extent does Spotify consider listeners' real musical tastes when making personalized recommendations? - in the sense of discovering how consumers are more likely to enjoy music if they have more control over their recommendations.

Looking at the figure 17- It seems odd that the more I listen to Spotify recommendations, the more I contribute to an algorithm that creates more similar suggestions - we can see that more than 50% have the notion that by choosing the music they like the best, they are contributing to a pattern that is easily collected by an algorithm, which keeps the recommendations accurate. Their majority answers,

“Somewhat agree” and “Strongly agree” rely on the idea that algorithms are efficient, but strange at times. It appears that either the mode (4,0) or the mean (3,6) of responses are grouped in the

“Somewhat agree” category. Also, looking at figure 18 - I want to branch out from my usual music routine and hear new music - over 50% of the respondents said they want to discover new music and change their musical routine. This figure has an average answer of "Somewhat agree" (3,9). Hence, participants are looking to step out of their usual musical taste, something that has been discussed in

Figure 17 - It seems odd that the more I listen to Spotify recommendations, the more

I contribute to an algorithm that creates more similar suggestions.

Figure 18 - I want to branch out from my usual music routine and hear new music.

26 the literature review: recommendations need to extend beyond usual patterns. This discovery of new music is, however, influenced by the algorithm's impact on people's perception of music taste.

Therefore, the more the algorithm can adapt to a person's taste, the more likely the recommendation is to be one that they like. The participants like the automated music suggestion process for finding new music, but with the perspective of always discovering new music.

Experience

Spotify has mastered the art of automating music recommendations with a user experience that feels familiar and allows users to discover new music. Thus, by projecting in-app activities into human traits and emotions, Spotify attempts to model user behavior in the app.

Based on figure 19 - As seen above using the Chatbot would be easy since there is no writing involved and the responses would be sent automatically - we can see that 78.5% of respondents agree that the Mary demo chatbot would be user-friendly, while 7.0% disagree, and 12.5% are either agreeing or disagreeing with mean score responses in “Somewhat agree” (4,0). In summary, most people believe that the chatbot in the demonstration format would be relatively easy to use.

According to figure 20 - Using Mary, the Chatbot on Spotify would require effort and time - approximately 46% disagreed with the notion that it would be time-consuming and wasteful, while only 27.5% thinking otherwise. However, there was a great abyss of agreement and disagreement on this question, which may be correlated with the percentage of respondents who had never dealt with a chatbot (59%). It is estimated that the mean of responses lies between "Somewhat disagree" and

"Neither agree nor disagree" (2,7). It is possible to conclude from the given answers that those who are not agreeing or disagreeing had less interaction with a chatbot than those who did and,

Figure 19 - As seen above, using the Chatbot would be easy since there is no writing involved and the responses would be sent automatically.

Figure 20- Using Mary, the Chatbot on Spotify would require effort and time

27 consequently, accurately saw the benefits of Mary as a practical tool to help people discover songs based on their moods.

Looking at these new figures, we can see that both show similar distributions. Based on figure 21- The Chatbot would enrich my experience with Spotify - 71.5% of respondents agree that this chatbot will be an excellent tool within Spotify, while 9.5% disagree and the rest are unsure. This response has a mean score between "Agree nor disagree" and "Somewhat agree" (3,8), and the mode of the answers is "Somewhat agree" (4,0), as seen in the skewed right distribution. Overall, this reveals the openness of the participants towards a bot such as this.

According to figure 22 - I think having a Chatbot within Spotify could help me keep track of my everyday emotions - most people agree that this chatbot would make them more aware of their feelings and moods on an everyday basis (54% of participants), with a mean score of "Neither agree nor disagree”

(3,3). There is a great deal of agreement about the usefulness of integrating the chatbot Mary into Spotify, but there was a slight decrease in the number of people who said the chatbot could be useful for tracking emotions on figure 22, indicating that most people agree that the tool is useful for detecting emotions, while a very small number disagreed.

Figure 21 - The Chatbot would enrich my experience with Spotify.

Figure 22 - I think having a Chatbot within Spotify could help me keep track of my

everyday emotions.

28 4.1.5. Music as a tool to express emotions

Throughout human culture, music is a source of health, well-being, communication, and self- expression: "Wherever there are human beings, there is music; and wherever there is music, there are emotions" (Patrik N. Juslin, 2019). In this way, music plays a crucial role in our emotional lives as well as in how we use and relate to it in our everyday lives. When listening to music on Spotify, participants were asked what their driving emotions are during the listening process.

Table 2 -Answers give to the question: “Think of the chatbot as a human being attempting to recommend a song or playlist based on your current mood. Please rank which of the following

emotions you would like the chatbot to consider.”

Among one of our last observations - Think of the chatbot as a human attempting to recommend a song or playlist based on your current mood. Please rank which of the following emotions you would like the chatbot to consider – it was concluded that about 66.5% of the first 133 participants identified

“Happiness” as the first emotion they wanted the chatbot to acknowledge when recommending music.

Among the second group of participants, 52 participants listed “Sad” and 54 participants listed

“Motivated” as their preferences. The third most popular choice was “Focused”, while the fourth most popular choice was “Bold”. Following that, participants placed “Nervous” (45 participants) and “Bold”

(42 participants). In second-last place, participants voted “Nervous”, and finally, a large majority of people voted “Angry” with 108 choices.

As we scale the emotions from “Happiness” to “Angry”, the questionnaire indicates that people are most interested in seeing the chatbot consider “Happiness”, “Sadness”, “Motivated”, and “Focus”

when suggesting music. “Angry” and “Nervous” are the least desirable emotions for a chatbot to consider. The result shows that when people are recommended music, emotions near the aggressive end of the emotion scale demotivate them.

29 4.1.6. To use or not to use Mary the Chatbot

In the last two questions, the survey aimed to understand if people were really willing to use a chatbot on Spotify, considering all the benefits it could bring regarding perception of personality traits on musical preferences. To begin with figure 23 - If there was a chatbot like Mary within Spotify, I would use it - on a scale of 1 to 10, answers from 0 to 3 were considered unreceptive, answers from 4 to 6 neutral, and answers from 7 to 10 receptive.

In a survey of 200 people, 40% felt neutral about using a chatbot such as Mary to humanize the suggestion algorithms, 37% were unreceptive, and 23% were receptive to the introduction of the bot.

However, 59% of respondents said they had never used a chatbot when asked if they had already used one. As shown below in figure 24, there may be a correlation between people who have never used a chatbot and unreceptive or neutral responses.

Figure 23 - If there was a chatbot like Mary within Spotify, I would use it

30 As mentioned above, the age factor might contribute to the unreceptive response towards the chatbot. As shown in figure 25, participants who were unresponsive to the Spotify chatbot were primarily older.

Figure 24 - Participants who have never used a chatbot and unreceptive responses.

Figure 25 - Age distribution plot of unreceptive participants to the chatbot

31 As for figure 26 - Instead of having this interaction as a permanent feature, I'd like to be able to activate or deactivate the chatbot as needed - 69% of respondents are in favor of having the option to enable or disable the chatbot feature, 21.5% are neutral, and 9.5% are indifferent. This shows that most people want control over this feature, which gives insight into how important it is to have control along with choices, when it comes to algorithms.

4.2. R

First, we examined the impact of X (personalized music recommendations) on Y (Customer Satisfaction, which is divided into four variables). After that, the interaction between X and the moderator variable M (chatbots displaying human-like cues). Tests of significance are used in statistics to determine whether to reject or accept certain claims based on the data (Serban, 2010). This project uses regression analysis to determine whether an independent variable significantly explains the variance of the dependent variable. F-statistics were used to develop the model in question, since it included more than one independent variable. It is also possible to calculate the F-statistic to determine multivariate significance.

The equation for F-statistic is:

SSRr represents the Sum square of residuals of the restricted model, and SSRu represents the Sum square of residuals of the unrestricted model. Additionally, P here indicates the number of predictor

Figure 26 - Instead of having this interaction as a permanent feature, I'd like to be able to activate or

deactivate the chatbot as needed

32 variables and N, the number of observations. The numerator is P, and the denominator is N-P-1, forming a comparable distribution, which also happens to be our degree of freedom.

• df1 = P: Degree of freedom 1

• df2 = N-P-1: Degree of freedom 2

Using the degree of freedom values, we can determine the F-critical value. If F-statistic > F-critical, the null hypothesis is rejected, which means that the independent variables are jointly significant in explaining the dependent variable's variance.

As part of this study, we will use a Probabilistic F-statistic, which contains a confidence interval of 95%

and an alpha value of 0.05. We reject the null hypothesis since the Probabilistic F-statistic is close to zero, indicating that the independent variables are significant in explaining the variance of the dependent variable.

4.2.1. Humanizing Spotify through a chatbot to improve the consumer experience

As a first analysis, we tested our first hypothesis that concrete (versus abstract) message frames would impact (a) Well-being, (b) Brand trust, (c) Brand loyalty, and (d) Perceived quality. Below is a table showing the results of these analyses using OLS (Ordinary Least Squares) statistical models:

Dependent Variables

R-squared Adj. R- squared

F-statistic Prob (F- statistic)

Wellbeing 0.865 0.863 632.5 1.03e-86

Brand Trust

0.866 0.865 641.0 3.28e-87

Brand Loyalty

0.924 0.923 1201 1.89e-111

Perceived Quality

0.889 0.888 791.5 3.58e-95

Table 3: Results of these analyses testing whether concrete (versus abstract) message frames would impact (a) Well-being, (b) Brand trust, (c) Brand loyalty, and (d) Perceived quality.

Based on the p-value (listed as Prob F-statistic) in the summary, we can reject or accept the null hypothesis. P-values indicate the probability that the null hypothesis (all regression coefficients are zero) is true. Based on all aspects, the p-values are close to zero, so the null hypothesis can be rejected.