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Decoding Musical Decisions: Navigating the Behavioral Economics of Music


By: James Brown, Bridget Chi, Rylie Delacruz, Rajeswari Ramaswamy, Hakan Unlu


What is BEM? 

In 2007, Radiohead sent shockwaves through the music industry with the digital release of their seventh album, In Rainbows. The digital release garnered entrepreneurial significance in the industry because Radiohead allowed fans to “pick your own price” for the digital download of the album…in other words, fans even had an option to download the album for free! Counterintuitively, the strategy worked. Although the average price that fans ended up paying is unknown, Radiohead declared they made more money from digital downloads of In Rainbows than for all of their other albums and the album’s overall sales ended up higher than previously expected (Bourreau et al., 2015). 


The Radiohead example raises questions about how people assign monetary value to music.  Traditional economic theorists would argue that consumers are rational decision makers seeking to spend the least to get the most out of a product or service. However, Dr Nikhil Masters, lecturer in the Department of Economics at the University of Essex, uses the Radiohead example to illustrate the shortcomings of traditional rational economic theories, and a need for a re- conceptualization of economics when applied to music. 


Enter the behavioral economics of music (BEM): a melding of behavioral psychology and behavioral economics as applied to music. Nikhil and fellow researchers have conducted an extensive systematic literature review (Anglada-Tort, Masters et al., 2023) exploring a myriad of phenomena intersecting music, cognition and consumer behavior, to better understand how consumers make decisions around music, including making seemingly sub-optimal decisions in their music purchases. 


Combining insights from economics and psychology, this novel research direction examines how heuristics, or mental shortcuts our brains use to make quick decisions, unconsciously sway our musical tastes. For example, the “availability bias” explains why familiar songs feel more likable (North & Hargreaves, 1995) and the “conformity bias” explains why we follow the herd in liking chart-topping songs. An examination of these heuristics also helps us tackle a few other musical conundrums, such as how musical tastes form and why they spread throughout social circles, or whether there is something special about complex classical compositions that makes people less likely to re-listen to classical tracks on demand.


Choice Overload

One specific area that Nikhil feels BEM can be applied to is decision-making in music listening choices. Whether buying an expensive concert ticket or watching a YouTube music video, each act of selecting, buying, and listening to music involves a decision. Like any other commodity, several factors come into play, but today’s music market presents its own challenges.


Streaming music is the predominant method of music consumption today (Pattison, 2024). Streaming services, such as Spotify, offer virtually endless options to their users, with over 100 million songs on their platform (Shewale, 2023). At first, it’s like being a kid in a candy store…so many great things to choose from! However, in reality, users may struggle to select a single song, unhappily aware that they’re leaving millions of others unheard and dissatisfied despite having clear favorites and playlists of songs that “hit home”. 


This paradox of having too many songs to choose from while still having “nothing to listen to” has kept economists and consumer psychologists busy looking for balances. On the one hand, there are instances where a large choice set enables consumers to find a product that resonates. They enjoy freedom of choice, an enhanced experience and overall satisfaction (Chernev et. al, 2015). However, with 100 million songs to choose from, individuals’ cognitive capabilities can quickly become overwhelmed (see Simon, 1955 on computing capacity), a phenomenon known as choice overload in consumer psychology. The reasons for anxiety due to choice overload vary from person to person. They could be due to time constraints or uncertainty about preferences or even feelings of accountability – especially when the music is shared with others (Chernev et. al, 2015).


Everybody deals with this unease in a different way. Some people resort to listening to familiar tracks over and over because selecting something new to try out is simply too daunting. Others switch from song to song, with the hope of finding something better, yet never actually listening to any one song to the end (Chernev et. al, 2015). Still others offload all responsibility to the streaming service’s recommendation algorithms and just listen to whatever the service chooses, regardless if they feel “fulfilled” with the result or not. The days of purchasing a single CD or record and happily listening to it over and over…are over!


Future Directions

BEM studies are searching for ways to reduce the adverse effects of choice overload in music streaming services. Organizing music according to categories such as mood or activity and considering the personality traits or musical expertise of the listener are some of the tactics that have been explored (Ferweda et al., 2019). Imagine a personalized playlist, optimized specifically for you, leaving you satisfied each time you put your headphones away!


What about using BEM to help with sticking to an instrument? It’s one thing to pick up an instrument casually in your youth, and quite another to stick to your decision long enough to master it. Practicing day after day requires active decision-making and “present-bias” is a form of procrastination where instant gratification overrides a previous decision (e.g., scrolling on your phone versus practicing). Behavioral economists are very interested in the ways humans procrastinate (Steel, 2007) and procrastination studies can help researchers in BEM understand how successful musicians practice self-control and introduce strategies that will prove beneficial in music education.


Tools from behavioral economics could be used to dismantle barriers to attending classical music concerts. These tools are aimed at changing social norms and include moving music venues out of concert halls and into parks or relaxing dress codes to encourage different demographics to experience classical music. Experimenting with advertising that challenges stereotypes associated with classical music is another strategy. Drake performing together with the London Symphony Orchestra, anyone?!



References 

Anglada-Tort, M., Masters, N., Steffens, J., North, A., & Müllensiefen, D. (2023). The Behavioural Economics of Music: Systematic review and future directions. Quarterly Journal of Experimental Psychology, 76(5), 1177-1194.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10119905/


Bourreau, M., Dogan, P., & Hong, S. (2015). Making money by giving it for free: Radiohead’s pre-release strategy for In Rainbows. Information Economics and Policy, 32, 77-93. https://www.sciencedirect.com/science/article/pii/S0167624515000244


Chernev, A., Böckenholt, U., & Goodman, J. (2015). Choice overload: A conceptual review and meta-analysis. Journal of Consumer Psychology, 25(2), 333-358. https://www.researchgate.net/publication/265170803_Choice_Overload_A_Conceptual_Review_and_Meta-Analysis


Ferwerda, B., & Tkalčič, M. (2019, June). Exploring online music listening behaviors of musically sophisticated users. In Adjunct Publication of the 27th Conference on User Modeling, Adaptation and Personalization (pp. 33-37). https://www.bruceferwerda.com/papers/2019_Ferwerda_UMAP.pdf


North, A. C., & Hargreaves, D. J. (1995). Subjective complexity, familiarity, and liking for popular music. Psychomusicology: A Journal of Research in Music Cognition, 14(1-2), 77. https://www.researchgate.net/publication/254734937_Subjective_complexity_familiarity_and_liking_for_popular_music


Pattison, S. (2024, January 11). 35 streaming services statistics you need to know in 2024. Cloudwards. https://www.cloudwards.net/streaming-services-statistics/ 


Shewale, R. (2023, December 28). Spotify stats for 2024 (subscribers, Revenue & Trends). demandsage. https://www.demandsage.com/spotify-stats/ 


Simon, H. A. (1955). A behavioral model of rational choice. The quarterly journal of economics, 99-118. https://www.jstor.org/stable/1884852


Steel, P. (2007). The nature of procrastination: a meta-analytic and theoretical review of quintessential self-regulatory failure. Psychological bulletin, 133(1), 65. https://www.researchgate.net/publication/6598646_The_nature_of_procrastination_a_meta-analytic_and_theoretical_review_of_quintessential_self-regulatory_failure_Psychol_Bull_133_65-94


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