Taste and the algorithm

Today, a consistent part of our everyday interaction with art and aesthetic artefacts occurs through digital media, and our preferences and choices are systematically tracked and analyzed by algorithms in ways that are far from transparent. Our consumption is constantly documented, and then, we are fed back through tailored information. We are therefore witnessing the emergence of a complex interrelation between our aesthetic choices, their digital elaboration, and also the production of content and the dynamics of creative processes. All are involved in a process of mutual influences, and are partially determined by the invisible guiding hand of algorithms. With regard to this topic, this paper will introduce some key issues concerning the role of algorithms in aesthetic domains, such as taste detection and formation, cultural consumption and production, and showing how aesthetics can contribute to the ongoing debate about the impact of today’s “algorithmic culture”

The Impact of Spotify’s AI-Driven Music Recommender on User Listener Habits

This study explores how Spotify uses AI-technology to collect data about the user’s music listening behavior and serve personalized music recommendations based on their music taste and listening habits. It also involves a quantitative survey to discover the impact these AI- driven algorithms have on the Spotify users, especially focusing on four carefully chosen aspects: the user’s satisfaction with the music recommendations, the correlation between their satisfaction and their user activity, their selectivity in song choices and their ways of discovering new music. The results from the survey indicates that there is an overall satisfaction with the music personalization, especially for the most active users. Also, their reports indicate that they prefer the mix between familiarity and music discovery, and that they don’t believe the recommendations have a significant impact on their selectivity

Mobile Music Business Models in Asia\u27s Emerging Markets

In the telecom business, there has been a heavy competition from Internet, media and handset vendors companies. These over-the-top (OTT) players offer compiling telecom services, cause a transformation in the telecom business ecosystem, and the most challenging services posed here are media services. China, India and Indonesia, as world’s emerging markets in Asia, are predicted to take the largest share in the global mobile traffic explosion by 2015. It is critical for mobile network operators (MNOs) in this region to explore strategy for mobile media services, as mobile broadband is likely preferred compared to fixed broadband. In this paper, we analyze and compare mobile music business models used in these markets and structure the relation models between the key actors, using Actors, Relations and Business Activities (ARA) model. We present the economic models that are emerging, and an insight of why and how these multitudes actors are betting on currently. We found that the MNOs generally have a much stronger position compared to their counterparts in the developed markets, and the personalization services, like ring-back tone, are still a huge success. The actors tend to deliver the services by their own, rather than to collaborate in a horizontal business setting

Motivations in the adoption and conversion of Music Freemium Services

Mestrado Bolonha em MarketingCom o recente avanço tecnológico, é possível ouvir música de novas maneiras. Isto resultou no aumento do valor de mercado da música e no surgimento de diversos serviços de streaming on-demand com o modelo de negócio freemium. Estes serviços têm sucesso, especialmente, quando os seus utilizadores convertem a sua subscrição de free para premium. O presente trabalho propõe-se a estudar quais as motivações que levam os consumidores a adotar uma plataforma de streaming de música, e quais as motivações e características de utilizador que levam à conversão para o serviço premium. Alguns estudos dedicaram-se a explicar o porquê desta conversão, mas pouco foi pesquisado no que toca às motivações dos consumidores para distinguir entre diferentes plataformas. Para aprofundar estas questões, este estudo analisa um conjunto de motivações e caraterísticas de utilizador como variáveis explicativas em conjunto, de forma original, não encontrada na literatura. Deste modo, os dados foram obtidos através de um inquérito online, com uma amostra de 231 utilizadores portugueses de plataformas de streaming. Os resultados principais apontam que a satisfação, valor percebido e ubiquidade são motivações estatisticamente significativas que influenciam positivamente a escolha de diferentes plataformas. Para além disto, as mesmas motivações, bem como a idade e ocupação (características de utilizador) mostraram-se impactantes no que diz respeito à conversão, sendo relevante do ponto de vista teórico e do ponto de vista prático. No entanto, os resultados destacam a influência negativa da satisfação e idade nesta compra. Isto significa que um utilizador altamente satisfeito não se converte e de modo semelhante, quanto mais velho for o utilizador, menos provável é que a compra ocorra. Não há evidência estatística que as motivações de descoberta, exclusividade, social e personalização e as restantes características de utilizador influenciem a conversão de utilizadores free em utilizadores premium.With the recent technological advancement, music is being experienced in new ways. This resulted in the rising value of the music market and the surge of diverse on-demand streaming services with the freemium business model. These services thrive especially when its users convert their subscription from free to premium. The current dissertation aims to study what motivations drive consumers to adopt different music streaming platforms and what motivations and user characteristics leads them to convert to the premium service. Several studies endeavoured on explaining this phenomenon, but little research was dedicated on what are the motivations for consumers to distinguish between different platforms. To enhance comprehension in this matter, this study analysis a group of motivations and user characteristics as explanatory variables together as a set, in a original way, not found on the literature. Thus, data was obtained via an online questionnaire, with a sample of 231 Portuguese users of streaming platforms. The main results suggest that satisfaction, perceived value and ubiquity are statistically significant motivations that positively influence choosing a different platform. Regarding subscribing to the premium service, the same motivations, as well as age and occupation (user characteristics) present influential results, which poses relevancy from a theorical point of view and managerial point of view. However, the findings highlight satisfaction and age as negative influences for this purchase. This means that highly satisfied free users don’t convert and similarly, the older the consumer, the less likely the conversion happens. No statistical evidence was found in discovery, exclusivity, social and personalization motivations alongside the remaining user characteristics for the conversion of free users into premium users.info:eu-repo/semantics/publishedVersio

The Algorithmic Black Box: Exploring the Impact of Spotify and TikTok on User Behavior

This master’s thesis takes us on a journey through the assumption of algorithms and data via a quantitative approach. This guides us through the complexities of Spotify and TikTok’s algorithm and data, illuminating the key concepts and ideas that are essential for understanding this fascinating and an important topic. As we read, we are struck by two important research questions: • RQ1: Can Spotify and TikTok users discover new music and content with the guidance of algorithms? • RQ2: Does the algorithm affect the user behavior’s data positively or negatively within these platforms? This study is based on the following hypotheses; users will have the ability to discover new music and sound easily on both platforms determined by the algorithm. Additionally, the user’s data within behavior will be more or less affected by the algorithm positively and negatively. The research will be conducted through quantitative methods utilizing a survey. In this case, Google Survey will be used for data collection from respondents and will explore user interactions with the recommendation algorithm on Spotify and TikTok, as well as examine consumer behavior and the perceived effects of the algorithms on personal data. Additionally, the study will consider the influence of the COVID-19 pandemic on platform usage

Supporting Personalized Music Exploration through a Genre Exploration Recommender

Recommender systems have been largely focused on the task of predicting users' current preferences and finding the most relevant items that users currently like. However, this approach is not sufficient as users may want to explore and develop new preferences, for example about a new genre. Allowing users to explore new preferences has many advantages, such as helping users to stay away from the so-called ``filter bubbles'', supporting new preference exploration and development, and promoting under-explored niche tastes, in addition to the mainstream preferences. Therefore, in this dissertation, we explore how recommender systems can be leveraged to support users' new preference exploration in the context of music genre exploration. The research takes a multidisciplinary approach in which we explore music recommendation algorithms and interactive exploration interface design for supporting music genre exploration, paired with insights from individual's music preference evolution and theories on decision making (such as digital nudges). For this purpose, we propose a music genre exploration tool and refine the tool over subsequent studies. We evaluate the music genre exploration tool with multiple single-session user-centric studies and one longitudinal user study on the long-term effectiveness of the tool to drive new preference exploration with various types of users’ objective behavior and their subjective user experience. From the studies, we find that users perceived the music genre exploration tool to be a new and helpful way to explore and develop new music tastes. By allowing users to make trade-offs between their current preferences and the new music genre they want to explore, the music genre exploration helps users make an easy personalized first step out of their comfort zone and towards the new preferences. The newly designed interactive exploration interface of the music exploration tool improves the usability and helpfulness of genre exploration by improving transparency, controllability and understandability. We further investigate individual differences during musical preference evolution by checking individuals' musical preference consistency and identify a relevant personal factor associated with this consistency (i.e., musical expertise). Our findings suggest that users with different musical expertise tend to show different musical exploration behavior. We further enhance the exploration tool with digital nudges to see if digital nudges can promote more exploration from users, and based on insights on individual differences, how this differs among individuals with different expertise levels. Based on our findings, we discuss opportunities and implications for future recommender systems to support new preference exploration and development

Cluster Analysis of Musical Attributes for Top Trending Songs

Music streaming services like Spotify have changed the way consumers listen to music. Understanding what attributes make certain songs trendy can help services to create a better customer experience as well as more effective marketing efforts. We performed cluster analysis on Top 100 Trending Spotify Song of 2017, with ten attributes, including danceability, energy, loudness, speechiness, acousticness, instrumentalness, Liveness, valence, tempo, and duration. The results show that music structures with high danceability and low instrumentalness increase the popularity of a song and lead them to chart-topping success

Music Streaming Services: Understanding the drivers of customer purchase and intention to recommend these services

Dissertation presented as the partial requirement for obtaining a Master's degree in Statistics and Information Management, specialization in Information Analysis and ManagementThe music industry has undergone strong changes in relation to its production, distribution and consumption habits, due to the exponential development of new technologies, namely streaming platforms. The fact that sales from physical copies continue to decline significantly made it mandatory for this industry to reinvent itself by introducing music streaming services as a key part of the development of its business. This study aims to understand the factors that influence the consumption of music through streaming platforms studying, particularly, the intention to purchase a paid version of a music streaming service and to recommend it. Therefore, an extension of the UTAUT2 model (version of the Unified Theory of Acceptance and Use of Technology, applied to the consumer side) was created. An online survey was used to collect data from 324 music streaming services users and the framework was tested using structural equation modelling (SEM). It also included in-depth semi-structured interviews in order to draw conclusions about the profile of the new music consumer. Our findings verify that habit, performance expectancy and price value play the most important role in influencing the intention to use a paid music streaming service. The intention to recommend these services was also confirmed. With this analysis, centred in UTAUT2 theory, we contribute with new insights about music streaming services consumer behaviour, providing several theoretical and practical implications to music streaming services providers