The Behavioural Economics of Music A framework for investigating music decision making

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The Behavioural Economics of Music

A framework for investigating music decision making

vorgelegt von M. Sc.

Manuel Anglada-Tort ORCID: 0000-0003-3421-9361

an der Fakultät I – Geistes- und Bildungswissenschaften der Technischen Universität Berlin

zur Erlangung des akademischen Grades

Doktor der Philosophie - Dr. phil. -

genehmigte Dissertation

Promotionsausschuss:

Vorsitzender: Prof. Dr. Stefan Weinzierl Gutachter: Prof. Dr. Jochen Steffens Gutachter: Prof. Dr. Daniel Müllensiefen

Tag der wissenschaftlichen Aussprache: 16. Oktober 2020

Berlin 2021

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Acknowledgements

I am grateful for the inspiration, opportunities and support that have enabled me to realise this thesis. I would like to express this gratitude in particular to Prof. Dr. Stefan Weinzierl for the opportunity to complete my PhD at the Technische Universität Berlin. I would also like to deeply thank Prof. Dr. Daniel Müllensiefen, who was so significant in my decision to pursue a PhD. From playing football together at Goldsmiths to our collaborations on many different research projects, I have learned so much from Daniel. The continuous support, motivation, and expertise which I have received from Prof. Dr. Jochen Steffens, have also been so important to guarantee the successful completion of this work. In addition, I wish to thank Dr. Nikhil Masters, whose critical approach and expertise in economics has made him truly indispensable in the consolidation of the framework presented in this thesis.

I gratefully acknowledge the funding for my PhD from the Studienstiftung des deutschen Volkes. The stability and prestige provided by this scholarship has been crucial to my research. I would like to further extend my sincere thanks to the collaborators that have contributed to the scientific publications conducted within this thesis, namely: Prof. Dr.

Adrian North, Dr. Amanda Krause, Dr. Diana Omigie, Dr. Sanfilippo, Steve Keller, and Tabitha Trahan. I have also been fortunate to work with some incredibly talented Masters students: Björn Thorleifsson, Thomas Baker, Till Noé, Kerry Schofield, Emily-Beth Hill, Heather Thuering, and Pattera Sutanthavibul; and I thank them for their enthusiasm and dedication.

It is really important to me to also acknowledge the support and love of my family - my parents, Lluís and Marta, and my sisters, Berta and Mei. None of this would have been possible without them giving me such a wonderful childhood and great education. My mother and grandfather first awakened my enthusiasm for music and my father taught me the most important thing: to observe the world around us and ask critical questions. I also wish to thank my friends in Barcelona, London, and Berlin for providing the happy distractions in and out of the research world enabling me to enjoy my life to the full. Finally, special thanks to my partner, Haia, for her unconditional love, support and valuable insights.

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Abstract

Music-related decision making encompasses a wide range of behaviours including those associated with music composition and performance, listening choices, music consumption, and decisions involving music education and therapy. Although research programmes in psychology and economics have contributed to an improved understanding of music-related behaviour, historically these disciplines have been unconnected. In this thesis, I present The Behavioural Economics of Music (BEM), a novel research framework that promotes the study of musical behaviour using the tools of behavioural economics. Behavioural economics aims to increase the explanatory power of neoclassical economics by relaxing the rationality assumptions of homo economicus, incorporating insights from an array of disciplines, including psychology, sociology, anthropology, biology, and neuroscience.

Thus, the BEM offers an empirically supported set of new concepts and methods that can prove particularly suited to study the multi-faceted nature of music. Ten scientific publications were conducted to support this thesis at three distinct levels. Firstly, two literature reviews helped in understanding the need and value of the BEM. Secondly, four empirical studies examined whether core concepts from behavioural economics, such as bounded rationality and cognitive heuristics, can increase our understanding of music decision making. Findings from these studies indicated that listeners are not utility maximisers who use all information and time available to make optimal musical choices.

Instead, they are boundedly rational and, therefore, limited by their cognitive ability, time, and information available. Finally, a further four studies focused on the application of the BEM to improve music-related decision making in the real world. Two studies investigated decision making in the context of choosing music for branding and advertising and two other studies explored alternative methods to examine responses to music in the real world.

Overall, these studies emphasize the synergistic benefits of the BEM to both psychologists and economists. Specifically, the BEM draws upon insights from a range of disciplines (including important work from music psychology and music cognition), whilst incorporating models from behavioural economic theory, thereby providing a framework that is wide in scope as well as internally consistent.

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Appendix E Names and titles matter: Linguistic fluency and the affect heuristic (S5) 161 Appendix F The effect of name recognition on listener choices (S6) 193 Appendix G Source effects on the evaluation of music for advertising (S7) 211 Appendix H The effect of music recognition on consumer choice (S8) 238

Appendix I The busking experiment: A field study (S9) 266

Appendix J Popular music lyrics and musicians’ gender over time (S10) 283

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Chapter 1 Introduction

This thesis is organised as follows. The following parts of Chapter 1 introduce two disciplines that have been long concerned with the study of music decision making, i.e., music psychology and economics. The chapter follows by introducing the field of behavioural economics, highlighting its potential benefits for music research, such as the role of bounded rationality and related concepts. The chapter ends by outlining the research questions and aims that guided this work. Chapter 2 summarises the ten scientific publications conducted to support this thesis (see Table 2.1 for a list of publications; see Appendix A-J for the full texts), emphasising their value to the BEM. Chapter 3 provides a discussion of this work, focusing primarily on contributions and future directions.

1.1 Decision making in music psychology

Music psychology is a branch of psychology and musicology that aims to understand the psychological processes by which music is perceived, processed, responded to, created, and integrated into everyday life (see Hallam, Cross, & Thaut, 2016; Tan, Pfordresher, & Harré, 2017; Deutsch, 2013, for reviews). Decision making is inherent to many of these processes.

In this thesis, this is referred to as music decision making - i.e., the cognitive process of evaluating and choosing between alternative options in any human behaviour related to music. This section introduces some of the most prominent areas in music psychology that rely on our understanding of music decision making.

Decision making is inherent in any creative process by which music is composed and impro- vised. The process of composing and improvising music can be understood as a complex sequence of judgments and decisions that involve both formal theoretical basis of music

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1.2 Music decision making in economics 2 theory and creative basis mainly relying on aesthetic evaluations (e.g., Maeshiro, Nakayama,

& Maeshiro, 2011). For instance, deciding how to frame a harmonic accompaniment in the context of a newly composed melody, how to extend an improvisation by using a motive heard earlier, or how to produce a hit song that results in millions of sales. Psychologists have begun to examine how composers make choices as part of the creative process (see Impett, 2016, for a review) or how musicians decide which note to play next while improvising (see Ashley, 2016, for a review). However, the decision making process underlying music composition and improvisation is still poorly understood and, therefore, could benefit significantly by considering insights from other disciplines, such as theoretical modelling and utility theory from economics.

Decision making also plays a fundamental role in music performance evaluation (see Waddell, 2018, for a review). Yet research on this topic within the field of music psychology raises serious questions about the reliability and consistency of music performance evaluation (Waddell, 2018). For example, jurors’ decisions in a high profile musical competition were significantly influenced by the order in which the candidates performed:

those who performed first had a lower chance to win the competition, whereas those who performed later had a higher chance (Flôres & Ginsburgh, 1996). Other studies have identified several non-musical factors that can significantly bias the evaluation of music performances, including musicians’ body movements (Wöllner & Behne, 2011), race and gender (Elliott, 1995), and physical attractiveness (Griffiths, 2008). These findings illustrate the need to better understand and improve decision making processes in the context of evaluating music performances, such as increasing awareness between experts or using evaluative methods that are less prone to human errors.

Music decision making is also central to the study of music preferences and listening behaviour (see Lamont & Greasley, 2016; Lamont, Greasley, & Sloboda, 2016, for reviews).

Advances in technology have played a major role in the way people listen to music in daily life, enabling them to listen to music in a wide variety of situations, such as whilst working, exercising, travelling, or relaxing (Lamont et al., 2016). In these contexts, researchers have identified several psychological needs that underlie listening behaviour, including distraction, motivation, attention, emotional regulation, and stress reduction (e.g., Greb, Schlotz, & Steffens, 2018; Linnemann, Wenzel, Grammes, Kubiak, & Nater, 2018;

Saarikallio & Erkkilä, 2007). Nevertheless, despite the wide range of psychological approaches that have been used to investigate music preferences and listening behaviour, there is currently no unified theory that has successfully addressed the complexities of this topic and there is no model that can predict accurately a person’s preference or choice for music at any given point in time (Lamont & Greasley, 2016).

Finally, the process of selecting music in applied contexts is heavily reliant on our understanding of music decision making. A clear example is the use of music in advertising and branding. In this context, choosing an effective music branding strategy can have a positive impact on consumers’ buying behaviour and attitudes towards brands (see Allan, 2007;

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1.2 Music decision making in economics 3

North & Hargreaves, 2008; Oakes, 2007, for reviews). Nevertheless, a failure to adequately use music can result in detrimental effects on communication effectiveness and consumer behaviour (Allan, 2007; Lantos & Craton, 2012). Moreover, industry professionals often rely on their gut instinct and personal experience to make musical choices (Schramm &

Spangardt, 2016). Thus, when choosing music for advertising is important to improve current practices by designing efficient, reliable, and unbiased methods.

Overall, music psychology has examined music decision making in a wide variety of situations. However, this body of research is still relatively young and would benefit from using a more sophisticated and unified understanding of the processes underlying human judgments and decision making. This could be achieved by incorporating knowledge from other disciplines that have been long concerned with the study of human decision making, such as economics.

1.2 Music decision making in economics

Independent from music psychology, music-related decision making has also been studied through the lens of economics (see Byun, 2016; Cameron, 2015, 2016; Krueger, 2005;

Tschmuck, 2017, for reviews). This research is mostly focused on economic decision making related to music, such as the behaviour of firms in the music industry (Burke, 1996;

Sweeting, 2013; Rayna & Striukova, 2009), the economy of live music events (Decrop &

Derbaix, 2014; Hiller, 2016; Holt, 2010; Larsen & Hussels, 2011; Mortimer, Nosko, &

Sorensen, 2012), predicting music popularity in the charts (Bradlow & Fader, 2001; Elliott

& Simmons, 2011; Hendricks & Sorensen, 2009; Stevans & Sessions, 2005; Strobl &

Tucker, 2000), music consumption (Byun, 2016; Cameron, 2015), and music copyright and piracy (see Oberholzer-Gee & Strumpf, 2009; Varian, 2005, for reviews).

This body of research provides valuable insights to understand key phenomena that influence music consumption and shape the music market. For example, studies have found associ- ations between economic conditions and changes in the characteristics of popular music over time (Maymin, 2012; Pettijohn & Sacco, 2009; Pettijohn, Eastman, & Richard, 2012; Zullow, 1991): songs with faster tempo were most popular in good economic and social times (Pettijohn et al., 2012), whereas pessimistic rumination in popular music lyrics significantly predicted changes in consumption expenditures and General National Product (GNP) growth (Zullow, 1991). The economic effects of music piracy (illegally downloading and sharing music) have also generated great interest amongst economists (see Oberholzer- Gee & Strumpf, 2009; Varian, 2005). This is, in part, because the controversial results in the literature: while some studies show a relationship between the rise of music piracy and the decline in music sales (e.g., Liebowitz, 2004, 2006), others found little evidence for this causal relationship (e.g., Oberholzer-Gee & Strumpf, 2009). Another area of interest in the economics literature is the growing importance of the live music business. Here, studies suggest that the large decline in artists’ income from record sales has led to the increase in

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1.2 Music decision making in economics 4

price for concert tickets (Larsen & Hussels, 2011; Mortimer et al., 2012; Tschmuck, 2017), which grew by 82% from 1996 to 2003 (Krueger, 2005).

The economic literature on music-related decision making relies on a simple but powerful model of behaviour put forward by standard economics: when making judgments and decisions, people do so by maximizing a utility function, using all information available, and processing this information with the appropriate time and mental resources (see Coleman & Fararo, 1992, for one of many reviews on rational choice theory). Thus, this body of research tends to assume that stakeholders such as composers, producers, artists, labels, and listeners are rational actors. This rational model of behaviour is useful in providing a coherent and internally consistent body of theory that offers rigorous and falsifiable models of human behaviour. For instance, using standard economics one can model a consumer’s rational choice between choosing to consume music over other goods, or the production and supply curves in a rational music market that leads to equilibrium price (Byun, 2016).

Overall, research on music decision making in economics provides highly valuable insights to understand how people consume music and key factors that shape the music market.

Nevertheless, this body of research focuses mostly on economic decision making and does not consider the psychological underpinnings known to be involved when people create, perform, listen to, and respond to music. Importantly, the assumption of rationality put forward by standard economics has been strongly challenged in the last years. For example, laboratory and field experiments in both psychology and economics show that when making judgments and decisions, people are limited by their mental capacity, use heuristics to solve complex problems, their preferences are inconsistent over time, and their choices are influenced by social information, their current emotional state, and the context (see Dellavigna, 2009, for a review).

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1.3 The Behavioural Economics of Music (BEM) 5

1.3 The Behavioural Economics of Music (BEM)

In recent years, researchers have begun to utilise theories and tools from behavioural economics to study music-related decision making (e.g., Anglada-Tort & Müllensiefen, 2017;

Lonsdale & North, 2012). Behavioural economics is a scientific discipline that integrates economics, psychology, and other social sciences to understand and improve judgments and decision making of individuals and groups in a variety of domains (see Angner, 2012;

Cartwright, 2018, for introductory books; see Dhami, 2016, for a rigorous and detailed analysis of the methodologies behind the field; see Kahneman, 2011; Tahler, 2015; Ariely, 2008, for informative books aimed to the general public). Behavioural economics arose from a general discontent with the rational model of behaviour, stimulating diverse streams of work that have expanded at a remarkable rate (Dhami, 2016). Thus, by increasing the psychological underpinnings of standard economics, behavioural economics offers a more realistic and comprehensive account of human judgments and decision making, generating more accurate predictions and suitable methods for the study of human behaviour. This thesis proposes The Behavioural Economics of Music (BEM), a novel interdisciplinary approach to study the multi-faceted nature of musical behaviour by using knowledge and tools from behavioural economics. The BEM emerges from the intersection between behavioural economics and music research, aiming to contribute bidirectionally to both fields (see Figure1.1).

Fig. 1.1 The Behavioural Economics of Music.

Heuristics and biases Social preferences

Music perception

Music composition

Peer effects Behavioural Music performance

Time preferences Dual process theory

Economics

Music Research

Music preferences

Music consumption

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When investigating music-related decision making, researchers from both economics and psychology stand to gain from utilising the behavioural economics toolkit. Economists studying music can benefit by moving away from the rigid neoclassical assumptions that music composers, performers and listeners are rational actors, and instead apply more empirically supported evidence when examining musical behaviour. Equally, since behavioural economics still maintains its economic identity in terms of having falsifiable models that are mathematically rigorous, psychologists can benefit from incorporating such theoretical approach to address key issues in music research that have eluded researchers so far.

Furthermore, behavioural economics is an established paradigm that has been hugely influential in a variety of domains, leading to new interdisciplinary fields. Examples include the behavioural economics of health (Blumenthal-Barby & Krieger, 2015; Rice, 2013), education (Jabbar, 2011), climate change and energy use (Arne Brekke & Johansson- Stenman, 2008; Frederiks, Stenner, & Hobman, 2015). At the same time, behavioural economics is being applied to a wide variety of institutions and companies all over the world, including governments, NGOs, and business entities such as Airbnb, Uber, and Google (e.g., Samson, 2017). Given that research in music decision making is still relatively young and has no research agenda dedicated to this area exclusively, there is great potential in incorporating insights from behavioural economics to study music decision making. This work can be an important step towards creating this.

Finally, today it remains unclear to which extent music may prove exceptional or congruent with general theories of human judgment and decision making. However, the rich, multisensory, aesthetic, social, and highly emotional nature of music may challenge some of these theories, enhancing their generalizability and scope. Music not only offers a unique spectrum of situations to study human decision making, but it is also an important cultural product, an artefact of society that is shaped with political and socioeconomic changes. Thus, investigating music decision making provides a novel testing ground for general theories that could reveal unique insights into behavioural economics.

1.3.1 Bounded rationality in music

Bounded rationality (Simon, 1955, 1982) is a core concept in behavioural economics that challenges the rational model of behaviour put forward by standard economics. According to bounded rationality, humans are limited by their mental capacity, available information, and time when making judgments and decisions. As a consequence, individuals seek decisions that satisfice (e.g., are good enough) rather than optimize (choosing the best possible decision). A central part of this thesis is to better understand bounded rationality in the context of music, as well as related concepts from behavioural economics that may help increase our understanding of music decision making. To illustrate how some of these concepts are particularly relevant in a music context, three examples are provided below:

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First, extensive empirical evidence shows that to make efficient judgments and decisions under bounded rationality, people rely on cognitive biases and heuristics. Heuristics are mental shortcuts used by individuals to simplify complex decisions into easier to calculate operations. Although they allow individuals to make decisions quickly and efficiently, heuristics can systematically fail and lead to cognitive biases (see Dhami, 2016; Kahneman, 2011; Dawes & Hastie, 2010, for reviews). Heuristics and biases are highly relevant in a music context, both in terms of the perception of music and how people respond to it. For example, songs with more repetitive lyrics, which are easier to process in terms of information, may be perceived as being liked more (processing fluency); individuals may falsely remember sounds that come easily to the mind (availability heuristic); and listeners may evaluate a music experience based on the most intense moment and the end (peak-end rule).

Second, dual-process theory proposes a cognitive architecture based on two systems of processing that support bounded rationality (Kahneman, 2003). Whereas the emotional System-1 is unconscious, implicit, automatic, effortless and rapid, the cognitive System-2 is conscious, explicit, controlled, effortful and slow (see Evans, 2008, for a review). Our capacity for mental effort is limited and, consequently, mental processes will be assigned to one of the two systems based on how much mental effort they require. Thus, dual-process theories exploring the interaction between emotional and cognitive processes in the brain can be useful for understanding music performance and listening behaviour. For example, to determine the extent to which musicians’ decisions while performing are conscious or unconscious, and how these decisions may be influenced by music expertise.

Finally, in 2007, the critically acclaimed band Radiohead surprised the music industry by offering their new album “In Rainbows” as a digital download using a pay-what-you-want (PWYW) agreement. Essentially, this meant that fans could pay as much as they liked for the album, including a zero option. Although at odds with neoclassical economic theory, in which consumers would download the album for free, many fans made voluntary payments for the album. One possible explanation for such generous payments under PWYW is that individuals exhibit social preferences, i.e., they care about the preferences of others (see Fehr & Rangel, 2011, for a review). Thus, social preferences are an example of how individuals do not always act as self-interested decision-makers and their decisions are influenced by the context and information available.

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1.4 Research questions and aims 8

1.4 Research questions and aims

This thesis had two main goals: (i) to gain a solid understanding of the role that behavioural economics can play to increase our understanding of music decision making, and (ii) to provide fruitful directions for future research. To achieve these goals, five research questions guided the present work:

• RQ1 - Where does music decision making sit within the music psychology literature?

• RQ2 - To date, which studies have utilised behavioural economics for research on music-related decision-making?

• RQ3 - How can insights from behavioural economics, such as bounded rationality, increase our understanding of musical behaviour?

• RQ4 - How can behavioural economics improve music-related decision making in real world applications?

• RQ5 - Which are the most fruitful areas for future research on the BEM?

Ten scientific publications were conducted to address these research questions (see section 2 for a summary of these studies highlighting their main contributions to the present thesis;

see Appendix A-J for the full texts). First, a bibliometric study visualizing all published literature on music psychology enabled the identification of an important gap in the music psychology literature, namely, a lack of a research agenda dedicated exclusively on music decision making (RQ1; see Appendix A for the full text). Second, a systematic literature review was conducted to provide an up-to-date account of all studies that have utilised behavioural economics for music research thus far (RQ2; Appendix B). The systematic review also identified fruitful avenues for future research on the BEM (RQ5). Furthermore, four empirical studies examined the role of bounded rationality and related concepts to increase our understanding of music decision making (RQ3; Appendix C-F). Finally, four studies focused on applying insights from behavioural economics to improve music-related decision making in real world situations (RQ4; Appendix G-J).

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Chapter 2 Scientific Publications

This thesis was written cumulatively and comprises a total of ten scientific publications (seven are published in scientific journals and three were in preparation at the time this thesis was submitted). The full texts of the ten publications are provided in the appendices of this thesis as pre-prints. This section briefly summarises these publications (see Table 2.1 for a list of publications), highlighting their main contribution to the BEM. Although the methods and scientific goals of these studies are diverse, they can be categorised into three groups depending on how they support this thesis: Why the BEM? (see section 2.1), bounded rationality in music decision making (see section 2.2), and real world applications (see section 2.3).

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10

Table 2.1 List of scientific publications.

ID Reference RQ

S1

S2

S3

S4

S5

S6

S7

S8

S9

S10

Anglada-Tort, M., & Sanfilippo, K. R. M. (2019). Visualizaing Music Psychology: A Bibliometric Analysis of Psychology of Music, Music Perception, and Musicae Scientiae from 1973 to 2017. Music & Science, 2, 2059204318811786. https://doi.org/10.1177/2059204318811786

Anglada-Tort, M., Masters, N., Steffens, J., North, A., & Müllensiefen, D. (in prep.). The Behavioural Economics of Music: Systematic Literature Review and Future Directions. Manuscript in preparation.

Anglada-Tort, M., & Müllensiefen, D. (2017). The repeated recording illusion: the effects of extrinsic and individual difference factors on musical judgments. Music Perception: An Interdisciplinary Journal, 35(1), 94-117.

https://doi.org/10.1525/mp.2017.35.1.94

Anglada-Tort, M., Baker, T., & Müllensiefen, D. (2019). False mem- ories in music listening: exploring the misinformation effect and individual difference factors in auditory memory. Memory, 27(5), 612-627.

https://doi.org/10.1080/09658211.2018.1545858

Anglada-Tort, M., Steffens, J., & Müllensiefen, D. (2019). Names and titles matter: The impact of linguistic fluency and the af fect heuristic on aesthetic and value judgments of music.

Psychology of Aesthetics, Creativity, and the Arts, 13(3), 277-292.

http://dx.doi.org/10.1037/aca0000172

Steffens, J., Till, N., & Anglada-Tort, M. (in prep.). I know that song: The effect of name recognition on listener choices when searching for music in playlists. Manuscript in preparation.

Anglada-Tort, M., Keller, S., Steffens, J., & Müllensiefen, D. (2020).

The impact of source effects on the evaluation of music for advertising:

Are there differences in how advertising professionals and consumers judge music? Advanced online publication, Journal of Advertising Research. http://dx.doi.org/10.2501/JAR-2020-016

Anglada-Tort, M., Schofield, K., Tabitha, T., & Müllensiefen, D. (in prep.). I’ve heard that brand before: The effect of music as a recognition cue to influence consumer choice. Manuscript in preparation.

Anglada-Tort, M., Thueringer, H., & Omigie, D. (2019). The busking experiment: A field study measuring behavioural responses to street music performances. Psychomusicology: Music, Mind, and Brain, 29(1), 46. https://doi.org/10.1037/pmu0000236

Anglada-Tort, M., Krause, K., & North, A. C. (2019). Popular music lyrics and musicians’ gender over time: A computational approach.

Psychology of Music. 0305735619871602.

https://doi.org/10.1177/0305735619871602

1

2, 5

3, 5

4, 5

’RQ’ denotes research question.

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11 2.1 Why the BEM?

2.1 Why the BEM?

To understand the need and value of the BEM, two literature reviews were conducted addressing three research questions (see Table 2.1; RQ1, 2 and 5). The first literature review identified a notable gap in the music psychology literature, showing that to date, there is no research agenda dedicated exclusively to decision making in the context of music (RQ1).

The second review constitutes one of the core parts of this thesis, providing an up-to-date account of studies utilising behavioural economics for research on music decision making (RQ2), as well as identifying fruitful avenues for future research on the BEM (RQ5).

2.1.1 Visualizing music psychology (S1)

This study aimed to analyse all literature published in the three most prominent scientific journals in the field of music psychology (see Appendix A for the full text). Namely, Psychology of Music, Music Perception, and Musicae Scientiae. Using all available literature in Scopus, a total of 2,089 peer-reviewed articles, 2,632 authors, and 49 countries were analysed, covering a period of 44 years (1973–2017). Visualization and bibliometric techniques were used to investigate the growth of publications, author and country productivity, collaborations, and research trends. Thus, the results of this study present objective and measurable patterns seen across the development of music psychology research included within these three journals. For example, from 1973 to 2017, there was a clear increase in music psychology research, with a total growth rate of 11%. A core feature of this paper is the visualization network map of music psychology (see Figure2.1). The network map shows the most influential keywords in the literature (i.e., keywords used by the authors to define their publications) as well as how they co-occur with others, creating different research trends and themes. For instance, the keywords “music” and “emotion” are the most influential keywords in the literature, which is in line with the general interest and significant increase in research on music and emotion (e.g., Eerola & Vuoskoski, 2013). The map also provides an overlay visualization that adds a time dimension to each keyword (i.e., colour-coding each keyword based on the average publication year), which suggests different trends in the popularity of each keyword over time. Thus, the network map is useful in summarizing the complex field of music psychology in a single picture.

Overall, this study contributed to the literature by providing the first large-scale bibliometric analysis that investigates general research trends and gaps in the field of music psychology.

Using bibliometric techniques to visualize the past and present of research in music psychology enables critical observations and conclusions, opening many interesting avenues for future research in the field. In the context of the BEM, this was important to identify a gap in the literature. Namely, there is currently no research agenda dedicated exclusively to study decision making in music. Despite this, decision making is inherent to many of the influential concepts and research trends identified in the study, including, music performance, creativity, improvisation, perception, music preference, music listening, and

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12 2.1 Why the BEM?

music therapy.

Fig. 2.1 Network visualization map of keyword co-occurrences in music psychology.

The map shows the 75 most influential keywords used by researchers to describe their articles and how often they co-occur with others, indicating main research trends and themes in music psychology. The width of the line shows the strength of the co-occurrence between keywords, while the size of the circle indicates the total

number of occurrences. The colour of the circle indicates the average year of publications.

2.1.2 The Behavioural Economics of Music (S2)

This paper is the core theoretical contribution of this thesis. The paper conducted a systematic literature review to provide an up-to-date account of studies utilising behavioural economics to investigate music decision making (see Appendix B for the full text). Using a robust search strategy that is highly representative of the behavioural economics literature, the systematic review identified a total of 33 papers within four BEM areas that readily apply to music decision making. Thus, the paper is organised around these areas, enabling the reader to fully understand the scope of existing research as well as giving direction for future research within each area. The main findings, organised by BEM area, are summarised below:

1. Heuristics and biases: the systematic review identified 16 studies applying six cognitive biases and heuristics to various aspects of musical behaviour (see Table 2.2 for definitions and music examples). Several studies confirmed that individuals rely on judgmental heuristics when listening to and evaluating music, allowing them to simplify complex decisions into easier-to-calculate operations, but also leading to systematic errors. Studies on processing fluency showed that fluency manipulations in music, such as repetition and consonance, can

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13 2.1 Why the BEM?

influence music perception and, in turn, affect preferential judgments. Finally, studies on framing effects found that presenting the same music stimuli with different contextual information can systematically change a person’s preferences for the music.

2. Social decision making: nine studies showed that music decision-making is largely influenced by social preferences and information. Firstly, social preferences, such as reciprocity and guilt, are important to understand consumers’ motivation to engage in different revenue models for music consumption, including voluntary payments for music. Social preferences can also help better understand pricing strategies in the concert industry. Secondly, peer effects can play a determinant role in music preferences and choices, which in turn can influence the music market and determine outcomes such as the next successful artist or hit song.

3. Behavioural time preferences: four studies found that behavioural time preferences can give a deeper insight into how music is valued and consumed over time. Two studies found that when consuming music online, consumers disproportionally prioritise im- mediate benefits over future gains, providing solid evidence for hyperbolic discounting in music consumption.

The two other studies focused on time preferences for music consumption in terms of its hedonic value. They showed that listeners’ ability to predict pleasure in their future music consumption is rather low and when choosing music repeatedly over time, listeners do not always choose music that maximizes their pleasure but instead seek variety.

4. Dual-process theory: four studies showed that exploring the interaction between System-1 and System-2 processes in the brain can help increase our understanding of how musicians make decisions while performing, as well as the mediating role of music expertise.

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14 2.1 Why the BEM?

Overall, the examination of these studies enabled to gain a solid understanding of the role that behavioural economics has played in music research thus far. Furthermore, it was clear from the review that the BEM is a relatively new approach and behavioural economics has just begun to be applied in the domain of music. For example, the vast majority of the retrieved studies were published in the last 10 years and over half of them in the last five years. The findings also suggested that the BEM is fairly multi-disciplinary. While half of the studies came from the music psychology literature, the other half came from behavioural economics. Finally, the paper concluded by providing fruitful ideas and directions for future research, both within the identified BEM areas and beyond.

Table 2.2 Heuristics and biases identified in the systematic review (S2).

Definition Music Example

Processing fluency

Availability heuristic

Represent- ativeness heuristic

Affect heuristic

Framing effect

Peak-end rule

Human tendency to evaluate easy-to-process information more positively than similar but more difficult-to-process information (Reber et al., 2004).

When judging the frequency and probability of events, people rely on the ease with which examples come to their minds (Tversky &

Kahneman, 1974).

People estimate the likelihood of an event by comparing it to an existing event of similar characteristics that already exists in their minds (Tversky & Kahneman, 1974).

Human tendency to rely heavily upon our emotional state when making judgments and decisions (Slovic et al., 2002).

People make decisions based on how the options are presented or “framed” (e.g., as a loss or as a gain) (Kahneman & Tversky, 1979).

People judge an experience largely based on how they felt at its peak (i.e., the most intense point) and at its end (Kahneman &

Fredrickson, 1993).

Songs with more repetitive lyrics, which are easier to process in terms of information, are perceived as being liked more (Nunes et al., 2015).

Listeners falsely remember sounds that come easily to their minds (Vuvan et al., 2014).

Stereotypes between music genres and fans can be misjudged (Lonsdale & North, 2011).

Individuals evaluate music based on an associated emotional feeling (Anglada- Tort et al., 2018)

Contextual information presented with music can systematically affect a person’s judgment of the music (North &

Hargreaves, 2004)

Listeners evaluate a music experience based on the most intense moment and at the end (Rozin, et al., 2004).

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15 2.2 Bounded rationality in music decision making

2.2 Bounded rationality in music decision making

To further develop the BEM within this thesis, four empirical studies explored whether bounded rationality and related insights from behavioural economics may prove valuable for music research (RQ3). The four studies showed that when making musical judgments and decisions, listeners are limited by their mental capacity (e.g., memory constraints), time, and information available (e.g., song titles, post-event information, or descriptions about the performer). Consequently, listeners rely on cognitive biases and heuristics that do not depend on the music stimuli themselves.

2.2.1 The repeated recording illusion (S3)

This study investigated the extent to which listeners are limited by memory constraints and the context when evaluating music performance (see Appendix C for the full text). To do so, a novel experimental paradigm was developed, namely, the repeated recording illusion.

In this paradigm, participants (N= 72) were told to listen to three “different” musical performances of an original piece. However, unbeknownst to them, they were exposed to the same repeated recording three times in succession. Each time, the recording was accompanied by a text suggesting a low, medium, or high prestige of the performer.

Participants evaluated the music using several rating scales (i.e., liking, timing, tone quality, pitch accuracy, emotional quality, and overall quality). The procedure was repeated using a piece of highly familiar rock music and a piece of unfamiliar classical music. Potentially related extrinsic factors (i.e., explicit information and repeated exposure) and individual differences were investigated.

Results showed that most participants (75%) believed that they had heard different musical performances while, in fact, they were identical. In the two music conditions, participants evaluated the same recording significantly more positively when it was presented with a high-prestige text compared to low and medium texts. However, the position of the recording only had a significant impact on the familiar music condition. To capture higher- order interactions between extrinsic and individual difference factors, a regression tree model based on permutation tests was computed. The dependent variable was a one-factor solution indicated by a Principal Component Analysis, where a negative score indicated an overall negative evaluation and a positive score an overall positive evaluation. The predictor variables were prestige effect, the position of the recording, music conditions (classical- unfamiliar vs. rock-familiar), and seven individual difference variables, including age, personality, and musicality. Figure2.2 shows the structure of the regression tree the model, which shows that only 3 of the predictor variables had a significant impact on performance evaluation, i.e., explicit information, repeated exposure, and the music condition. Note that none of the individual differences were significant predictors.

Overall, these findings highlight the fallibility of music evaluation and support the notion of bounded rationality in musical behaviour, showing that musical judgments are limited by

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memory constraints, cognitive biases, and the context. The influence of explicit information and the partial effect of repeated exposure are discussed in terms of the anchoring heuristic (Tversky & Kahneman, 1974) and processing fluency (Reber, Schwarz, & Winkielman, 2004).

Fig. 2.2 The influence of non-musical factors on music performance evaluation.

The regression tree model is useful in identifying the most predictive variables influencing music performance evaluation, as well as specific conditions that lead to particularly high (left node, 3) and low (right node, 9) ratings. The tree model can be interpreted by starting at the top and following each branch down, to arrive at a

terminal node. A path to a terminal node describes the interaction of experimental conditions that lead to a particular subset of ratings.

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2.2.2 False memories in music listening (S4)

When people listen to music or experience music in a live performance, they are normally exposed to related information at some point after the event. This study examined for the first time whether post-event misinformation can induce false memories in music (see Appendix D for the full text). Though misinformation effects have been demonstrated extensively within visual tasks, they have not yet been explored in the realm of non-visual auditory stimuli. Besides, the study explored individual difference factors potentially associated with false memory susceptibility in music, including age, suggestibility, personality, and musical training. In two music recognition tasks, participants (N = 151) listened to an initial music track, which unbeknownst to them was missing an instrument.

They were then presented with post-event information which either did or did not suggest the presence of the missing instrument. The presence of misinformation resulted in significantly poorer performance on the music recognition tasks (d = .43), suggesting the existence of false musical memories. A random forest analysis indicated that music expertise was not significantly associated with misinformation susceptibility. These findings support previous research on the fallibility of human memory and demonstrate, to some extent, the generality of the misinformation effect to a non-visual auditory domain. In the context of the BEM, this is important to further support the notion of bounded rationality in music decision making, in particular demonstrating the fallibility of memory-based judgments of music.

2.2.3 Names and titles matter: Linguistic fluency and the affect heuris- tic (S5)

This study manipulated the song titles and artist names presented with music to examine the influence of two well- known heuristic principles on aesthetic and value judgments of music:

processing fluency (Experiment 1) (Reber et al., 2004) and the affect heuristic (Experiment 2) (Slovic, Finucane, Peters, & MacGregor, 2002) (see Appendix E for the full text). In Experiment 1, the same music excerpts were presented with easy-to-pronounce (fluent) and difficult-to-pronounce (disfluent) names. The names consisted of a list of Turkish names that were shown in a previous study to be fluent or disfluent to English speakers (Shah &

Oppenheimer, 2007). Native English-speaking participants (N= 48) listened to the music stimuli and provided evaluations on different scales measuring aesthetic properties (e.g., like, emotional expressivity, quality) and subjective value of the music (e.g., likelihood to attend a concert of the artists or to recommend the song to a friend). Results indicated a main significant effect of fluency. In particular, participants evaluated the same music excerpts significantly more positively when presented with fluent names than when presented with disfluent names.

In Experiment 2 (N= 100), the same procedure was used, but instead manipulating the emotional content of the titles. Thus, the music excerpts were presented with positive (e.g.,

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Kiss), negative (e.g., Suicide), and neutral (e.g., Window) titles. This time, at the end of the experiment, participants also performed an unexpected free recall task (i.e., write down the songs they remembered). In both aesthetic and subjective value evaluations, presenting the music with negative titles resulted in the lowest judgments. When looking at the effects of emotionality on memory, results showed that music excerpts presented with neutral and negative titles were remembered significantly more often than positive titles.

Overall, these findings suggest that like any other human judgments, evaluations of music also rely on heuristic principles that do not necessarily depend on the aesthetic stimuli themselves. These heuristics operate even when the information processed is minimal, such as changing the linguistic properties of titles presented with music.

2.2.4 The effect of name recognition on listener choices (S6)

When searching for and choosing music in playlists, individuals may rely on judgment heuristics to make fast (in terms of computing time) and frugal (in the use of information) decisions. This study addressed this issue by investigating for the first time the role of the recognition heuristic on musical choices when listeners search for music in playlists (see Appendix F for the full text). The recognition heuristic states that when people are faced with recognised and unrecognised options, they infer that the recognized one has the higher value with respect to the criterion being judged and, therefore, they tend to choose it (Goldstein & Gigerenzer, 2002). In particular, the study extended the paradigm used in Oeusoonthornwattana and Shanks, 2010 to a listening task with 10 alternative choices, simulating a common listening playlist. Before the main experimental task, participants (German and English speakers) had to learn a list of Spanish names. This manipulation made it possible to create playlists using novel music paired with Spanish names that had been previously learned (i.e., recognisable) or were completely novel. In the main choosing task, participants were presented with ten songs in a playlist format and had to choose their favourite five. To study the role of recognition-based heuristics in the presence and absence of music information, participants searched for and selected music in two playlist conditions:

a titles-only condition (where they could only choose music based on visual cues – i.e., the Spanish names) and a titles-and-music condition (where they could choose music based on both visual and auditory cues – i.e., they could also listen to the music).

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Figure 2.3 depicts the mean choice proportion of music clips paired with learned and novel names in the two choosing conditions. Results confirmed that there was a significant effect of name recognition in the two choosing conditions, but this effect was larger when participants chose music based on visual information only (titles condition). Moreover, participants’ preferences for the selected music were also influenced by recognition - i.e., the same music clips were significantly more liked when paired with learned names than when paired with novel ones. These results show that listeners rely on the recognition heuristic when both deciding which songs to choose in a playlist and developing music preferences.

Fig. 2.3 Mean choice proportion of music clips when paired with learned and novel names in both playlist conditions (Error bars represent 95% CI).

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Listeners rely on recognition cues when searching for and choosing music in the two playlist conditions.

However, the effect of name recognition was larger when listeners chose music only based on visual cues (title only) than they chose music based on both visual and music cues.

Mean choice proportion of music clips

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20 2.3 Real world applications

2.3 Real world applications

The last part of this thesis focused on the application of the BEM to improve music-related decision making in the real world (RQ4). Two studies applied insights from behavioural economics to better understand the decision making process to select music for branding and advertising, whereas the other two studies explored alternative methods to examine music preferences in the real world, including field research and naturalistic data approaches.

2.3.1 Source effects on the evaluation of music for advertising (S7) This study focuses on improving music-related decision making in branding and advertising (see Appendix G for the full text). Music choices can have profound effects on brand communications, but the process of evaluating and selecting music for advertising and branding is poorly understood. When choosing music for advertisements, professionals are influenced by a large number of factors that could impair their judgment. Based on insights from behavioural economics, this study examined source effects in the evaluation of advertising music by professionals and nonprofessionals (a group of general consumers). In experiment 1, a group of advertising and marketing professionals listened to and evaluated music from three assigned sources: generic music libraries, music commissioned by production companies, or "real" artists (performing artists in the market). In experiment 2, the same procedure we repeated with a sample of general consumers. Results showed that advertising professionals gave significantly more favourable evaluations— higher in quality, authenticity, and expected cost— when they thought the music was sourced from performing artists compared with less credible and attractive sources. In contrast, consumers were not affected by source cues at all. Importantly, ad professionals were more aware of the influence of source cues than the group of consumers (see Figure 2.4), highlighting the difficulty for domain experts in advertising to build up effective cognitive defences against source effects. The differential effects of music source in the two groups could prove costly for brands. Professionals may recommend that their clients pay a premium for music coming from performing artists, but brands may see little or no added benefits if the source of the music does not matter to the listening public. Potential solutions to mitigate source effects include increasing awareness among professionals and measuring the impact of advertising music and source on target consumers.

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Fig. 2.4 Awareness of sources effects in consumers and ad professionals.

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4

2

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Consumers Professionals

Advertising professionals were significantly more aware of the influence of source effects when choosing music for ads than the group of general consumers. This suggests that for domain experts in advertising it is difficult to build up effective cognitive defences against this bias. In the Figure, violin plots are used in addition to box plots to show the probability density of the data at different values (smoothed using a kernel density estimator).

*** Denotes that the difference between groups is highly significant, as indicated by an independent t-test.

2.3.2 The effect of music recognition on consumer choice (S8)

This study is another example of how insights from behavioural economics can be successfully applied to music decision making in the context of branding and advertising. In particular, two experiments aimed to quantify the effectiveness of using music as a recognition cue to influence consumer choice by means of the recognition heuristic (see Appendix H for the full text). A pilot study was conducted (N= 2,854) to select 24 unfamiliar excerpts of advertising music and 24 unfamiliar brands. Prior to the main experimental task, participants memorised part of these unknown music clips. In a choice task, participants were then presented with pairs of brands, one presented with previously learned music and the other with novel music. Their task was to choose which brand they would purchase when buying different products (e.g., headphones, cameras). Results revealed that pairing brands with music that can be recognized by target consumers increased the likelihood that they will choose the brand by 6%, which corresponds to a small but significant effect size

****

Source effect awareness

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(d = .21). Furthermore, music preferences were a key moderating factor in the success of recognition-based heuristics. Exploratory results indicated that participants only relied on music recognition when they liked the music, whereas recognition-based heuristics did not play an influential role when the music was disliked (see Figure 2.5). Therefore, when using music to influence consumer behaviour, it is important to consider how recognition cues are processed in combination with other information, such as music preferences. This is valuable to inform brands in terms of measuring the value of their investment when working with music.

Fig. 2.5 Mean choice proportion of brands paired with learned and novel clips when the music was liked and disliked (Error bars represent 95% CI.).

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Dislike Like

Music recognition Novel Learned

Music recognition only had a significant effect on participants’ choices when the music was liked, whereas recognition-based heuristics did not play a significant role when participants disliked the music. The relative difference between choosing a brand paired with a learned and a novel music clip when the music was liked

was 18%, whereas when the music was disliked the difference was 5.1%.

Mean choice proportion of brands

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2.3.3 The busking experiment: A field study (S9)

This study applied methods from behavioural economics to a different music problem (see Appendix I for the full text). That is, what makes a successful street musician? And which aspects of the performative act might influence people’s economic responses? To address this question, a field experiment was conducted with a professional busker in the London Underground over the course of 24 days. The study primary aim to investigate the extent to which performative aspects influence behavioural responses to music street performances.

Two aspects of the performance were manipulated: familiarity of the music (familiar vs.

unfamiliar) and body movements (expressive vs. restricted). The amount of money donated and the number of donors were recorded. A total of 278 people donated over the experiment.

The music stimuli, which was selected in a previous study to differ only in familiarity, had been previously recorded by the busker. During the experimental sessions, the busker lip- synced to the pre-recorded recordings. Thus, the audio input in the experiment remained identical across sessions and the only variables that changed across conditions were the familiarity of the music and the expressivity of performed body movements. The results indicated that neither music familiarity nor the performer’s body movements had a significant impact on the amount of money donated or the number of donors. Importantly, the results do not support previous literature investigating the influence of familiarity and performers’ body movements, typically conducted in laboratory and artificial environments.

The findings are further discussed with regard to potential extraneous variables that may be crucial to control for in similar field experiments (e.g., location of the performance, physical appearance, and the bandwagon effect) and the advantages of field versus laboratory experiments.

2.3.4 Popular music lyrics and musicians’ gender over time (S10) This study applied a naturalistic data approach to investigate preferences for popular music in the UK over time (see Appendix J for the full text). Data on the singles sales charts from 1960 to 2015 was analysed as a proxy of music preferences. Note that singles sales charts is determined by weekly sales, downloads, and streaming of music. With this data, the study focused on how the gender distribution of the United Kingdom’s most popular artists has changed over time and the extent to which these changes might relate to popular music lyrics.

Using data mining and machine learning techniques, all songs that reached the UK weekly top 5 sales charts from 1960 to 2015 were analysed (4,222 songs). A computational analysis of the lyrics was conducted to measure a total of 36 lyrical variables per song. Results showed a significant inequality in gender representation on the charts. However, the presence of female musicians increased significantly over the period covered in the study.

The most critical inflection points leading to changes in the prevalence of female musicians were in 1968, 1976, and 1984. Linear mixed-effects models showed that the total number of words and the use of self-reference in popular music lyrics changed significantly as a function of musicians’ gender distribution over time, and particularly around the three

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critical inflection points identified. One of the most interesting trends found in the study is shown in Figure 2.6: Regardless of gender, there was a drastic increase in the total number of words over time, whereas the diversity of vocabulary (i.e., the number of different words in a song divided by the total words) decreased significantly over time, suggesting that UK popular music lyrics have become more repetitive over time. The use of data mining and machine learning techniques (e.g., classification tree models and random forest) offered several advantages in comparison to the statistical tools used in earlier studies.

Fig. 2.6 Mean words per song (left) and diversity of vocabulary (right) in UK popular music lyrics from 1960 to 2015.

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1960 1980 Year

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There was a drastic increase in the total number of words used in popular songs from 1960 to 2015 (left plot).

In contrast, when looking at a measure of the diversity of vocabulary (i.e., the number of different words divided by the total words in a song), the results showed that popular songs became less varied and more repetitive over time. This finding occurred regardless of the gender of the artists or band. Note that Both

indicate bands or artists with both female and male members.

Mean words per song Diversity of vocabulary

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Chapter 3 Discussion

This section starts by discussing the main theoretical and practical contributions of this thesis, with a focus on what have we learned from applying behavioural economics in the context of music. The section ends with discussing directions for new insights and valuable future research and, finally, concludes.

3.1 Theoretical contributions

The main theoretical contribution of this thesis is the conception of the BEM, an interdisciplinary but unified framework with which we can increase our understanding of musical behaviour (see Figure 1.1). Two literature reviews and eight empirical investigations (see Table 2.1 for a list of publications; see Appendix A-J for the full texts) demonstrate the value and potential of this novel approach.

The BEM contributes most significantly to the existing bodies of music research in both standard economics and psychology. Economists interested in music will benefit by moving away from the more rigid assumptions of standard economics and consider the psychological underpinnings known to be involved in musical behaviour. For example, in several studies conducted within this thesis, we learned that listeners are not utility maximisers who use all information and time available to make optimal musical choices.

Instead, there are several psychological constraints that limit their ability to evaluate and choose music, such as memory and the contextual information often presented with music.

Incorporating these insights will help building a more realistic and comprehensive account of music-related decision making.

The Behavioural Economics of Music A framework for investigating music decision making