Music Platforms Research Articles

Performance of complementor’s complementary goods on platform ecosystems: the perspective of individual complementors' complementary goods development behavior

The purpose of our study was to examine how individual complementors' development behaviors (specifically, diversity- and distinctiveness-oriented development behaviors) impact the performance of complementary goods on the platform. To achieve this, we employed fixed-effects regression analysis and utilized panel data obtained from the free music platform “DOVA.” This study examines two aspects of the performance of complementor’s complementary goods: the highest performance related to the success of the platform ecosystem and the lowest performance related to platform ecosystem sustainment. The findings indicate that a high focus on diversity-oriented development behavior is positively correlated with the complementor's highest performance and negatively correlated with complementor's lowest performance. Additionally, distinctiveness-oriented development behavior also positively correlated with complementor's highest performance. However, low distinctiveness-oriented development behavior is negatively correlated with complementor's lowest performance. We reveal desirable development behaviors for complementary goods related to both content innovation and platform ecosystem sustainability. Based on these understandings, platform owners can target platform governance.

Listening Habits of the Audience: Traditional Radio or Music Platforms? A Study About the Listener

Dijital ortamların ve yeni medya mecralarının her geçen gün daha da fazla günlük hayata dâhil olduğu bugünlerde, birey için geçmişin sesli içerik dinleme ve hayata dâhil olma alışkanlıklarından radyonun yeni düzende yerini araştırmak önem taşımaktadır. Yeni medya ve geleneksel medyanın aynı anda tüketilmeye başladığı ve kullanım oranlarının her geçen gün arttığı günümüzde, geleneksele olan ilgi eski günlerini aramaktadır. Bu çalışmada geçmişin dinleme alışkanlıklarından olan radyonun günümüz yeni medyasında halefi olarak yer alan müzik platformları gençlerin dinleme alışkanlıklarına yönelik bir saha çalışması ile belirlenmeye çalışılmıştır. Çalışmada örneklem alınan grubun dinleme eyleminde değişen alışkanlıklarına dair sorular ile veriler elde edilmeye çalışılmıştır. Bu kapsamda araştırmacılar tarafından geliştirilen anket formu kullanılmıştır. Araştırma bulgularına göre radyo üzerinden müzik dinleyen kitle sohbetli program yapısı ve müzik çeşitliliğinden dolayı radyoyu tercih etmektedir. Müzik platformu üzerinden sesli içerik dinleyen kitle müzik dinlemeyi kişiselleştirebilme ve reklamsız içerik yapısı nedenlerinden dolayı müzik platformunu tercih ettikleri tespit edilmiştir. Müzik platformu radyodan daha fazla tercih edilmektedir. Mevcut sonuçlardan yola çıkarak radyo ve müzik platformlarının dinleyiciye ulaşmasına ve ileriki bilimsel çalışmalara katkı sağlama çerçevesi geliştirilmeye çalışılacaktır.

Unveiling the melodic matrix: exploring genre-and-audio dynamics in the digital music popularity using machine learning techniques

PurposeThis paper aims to explore factors contributing to music popularity using machine learning approaches.Design/methodology/approachA dataset comprising 204,853 songs from Spotify was used for analysis. The popularity of a song was predicted using predictive machine learning models, with the results showing the superiority of the random forest model across key performance metrics.FindingsThe analysis identifies crucial genre and audio features influencing music popularity. Additionally, genre specific analysis reveals that the impact of music features on music popularity varies across different genres.Practical implicationsThe findings offer valuable insights for music artists, digital marketers and music platform researchers to understand and focus on the most impactful music features that drive the success of digital music, to devise more targeted marketing strategies and tactics based on popularity predictions, and more effectively capitalize on popular songs in this digital streaming age.Originality/valueWhile previous research has explored different factors that may contribute to the popularity of music, this study makes a pioneering effort as the first to consider the intricate interplay between genre and audio features in predicting digital music popularity.

Music recommendation systems in music information retrieval: Leveraging machine learning and data mining techniques

Music Information Retrieval (MIR) has become a pivotal area of research with the rise of digital music platforms, enabling personalized music recommendations to enhance user experience. This paper explores the integration of machine learning and data mining techniques in music recommendation systems. We discuss user-based and item-based collaborative filtering, matrix factorization methods like Singular Value Decomposition (SVD) and Alternating Least Squares (ALS), and content-based filtering that incorporates audio feature analysis, metadata, and lyrics analysis. Additionally, we delve into hybrid recommendation systems, combining collaborative and content-based approaches using advanced models such as neural networks and hybrid autoencoders. Our finding show that, hybrid systems provide the most accurate and personalize recommendations, albeit requiring significant computational resources. Practical applications from platforms likes Spotify and Pandora illustrate the effectiveness of these approaches in real-world settings.

The Inheritance and Development of Northern Shaanxi Folk Songs in the Era of Big Data

With the arrival of the big data era, the inheritance and development of northern Shaanxi folk songs are facing unprecedented opportunities and challenges. From the perspective of big data, this paper analyses the communication status of northern Shaanxi folk songs in contemporary society, discusses the impact of the big data era on the inheritance and development of northern Shaanxi folk songs, and puts forward a series of innovative inheritance and development strategies. Firstly, this paper gives a brief introduction to the concept and characteristics of the big data era, and elaborates on the historical and cultural value of northern Shaanxi folk songs. Through the data mining of network platforms and field research, the communication characteristics of northern Shaanxi folk songs are analysed, and it is found that its communication channels are wide and the audience groups are diverse, but at the same time, it also faces the dilemma of the traditional inheritance method and the problem of low acceptance of the younger generation. In view of these problems, this paper proposes a series of inheritance and development strategies. In terms of dissemination, it is suggested that new media channels such as digital music platforms and online short videos be used for promotion; in terms of education and training, it is advocated that Shaanxi folk songs be integrated into school music education and professional training courses to cultivate a new generation of Shaanxi folk song inheritors; in terms of artistic innovation, it is encouraged that Shaanxi folk songs be combined with modern art forms, and that new Shaanxi folk songs with the characteristics of the times be created. In order to verify the feasibility of the proposed strategy, this paper selects two successful cases to introduce and analyse, summing up a series of valuable experience. Finally, this paper summarises the whole paper and looks forward to the future inheritance and development of northern Shaanxi folk songs in the era of big data.

Folk music: the bedrock of Chinese cultural traditions / Música folk: fundamentos de las tradiciones culturales chinas

Folk music is an integral part of any national art and contributes to the cultural development of each country. This paper is aimed at studying aspects of Chinese folk music, which is the bedrock of Chinese cultural traditions. This paper conducted a sociological survey of 142 students in their third year of study. The results showed that 37% of the respondents believe that traditional music is a folk art passed down from generation to generation. The most important function of folk music, according to students’ opinions, is the preservation of cultural traditions based on historical events (27%). Due to the fact that respondents perceive folk music as the most harmonious in piano instruction (36%), the tools for its integration in piano lessons have been developed. These tools include selecting a music platform, providing theoretical information in the public domain, developing improvisation skills based on folk compositions, using online platforms to share experiences and knowledge between students and teachers and analysing contemporary Chinese compositions. In addition, all mentioned applications have been tested to measure their effectiveness. The best results of using folk compositions in piano instruction were achieved in group #5 (100%), which can be explained by knowledge gained in the first and second terms.

Mirroring the inequalities of mainstream music platforms: popularity, revenue, and monetization strategies on Bandcamp

ABSTRACT In the era of digital music streaming, understanding the monetization and popularity dynamics of online audio distribution platforms is crucial for both researchers and music industry professionals. While much of the existing research has focused on mainstream platforms such as Spotify and Apple Music, we study Bandcamp, an alternative marketplace that, among other things, empowers independent artists to upload and sell their music. Leveraging a rich dataset of one million Bandcamp sales, we investigate the distribution of popularity and revenues among artists on the platform. Our analysis reveals a substantial level of inequality, with a small core of musicians concentrating most of the purchases and revenues. Surprisingly, our results also indicate that many artists adopt a monetization strategy that involves devaluing their products, potentially leading to a cherry-picking of songs by consumers. These findings shed light on the challenges faced by alternative music distribution services and emphasize the need for further exploration of a fairer and more sustainable business model for independent artists.

API integration and organisational agility outcomes in digital music platforms: A qualitative case study

Organisations deploy digital platforms to maximise value and transform their businesses. The success of most platforms is attributed to Application Programming Interfaces (APIs), the protocols enabling different software to communicate with each other. However, previous research on APIs has predominantly focused on the technical dimensions, such as design, and unintentionally neglected other social areas, such as organisational outcomes. This study seeks to advance organisational API research by adopting an agility perspective to explore the agility outcomes after API integration. Through rich qualitative data from a music digital firm, the findings revealed four primary agility outcomes: customer agility in the form of swift customer feedback, operational agility in the form of improved business process and delay reduction, partner agility in the form of embracing flexibility in processes and structures and expanding their ecosystem and decision agility in the form of fast decision making. A model showing the interplay and interdependencies of the agility outcomes was developed and provided depth and clarity to the findings. This study extends the literature by establishing how API integration influences organisational agility under conditions such as possessing capabilities and managing tensions during the integration process.

Thai Rap Songs Through the World Englishes Approach: Code-Switching and Nativization

Abstract This study examines the English elements and nativized English features present in recent Thai rap songs. The source data comes from a Thai rap playlist called Thai Rapper on the online music platform Spotify. All English units found in the lyrics were manually counted and classified. The results diverged from most studies of Thai-English code-switching in Thai media in that, among the English elements considered, sentences were employed most frequently. Among nativized English features, semantic shift was most frequently employed. I argue that the local and global English identity flows (Pennycook 2009) index a shift in the Thai rap community toward assimilation of their Thai hip-hop identity into global street culture, a phenomenon becoming more prevalent among today’s Thai youth.

Reflections on the Dissemination and Development of Ethnic Music Culture in the Context of New Media

Modern digital music platforms and social media tools have become the mainstream form of music and culture dissemination. These platforms can optimize the efficiency of folk music dissemination, and at the same time, integrate folk cultural resources to present to people in a three-dimensional way. New media platforms integrate traditional music culture and modern art culture, and lay an important foundation for the globalization of folk music. The article explores the characteristics of ethnic music communication in the context of new media, analyses its main ideas and future development trends, with a view to providing important references for professionals.

浅谈线上音乐平台中的韩文歌词汉译

In recent years, cultural exchanges between China and South Korea are no longer as active as before due to China's growing awareness of protecting local culture. However, “Korean wave” culture, especially Korean pop songs such as K-POP, remains hugely popular in China. On China's major online music platforms, Korean songs account for a large proportion of listeners. Music is a borderless language, and lyrics are an important means to promote music. Therefore, it is of great significance to study the Chinese translation of Korean lyrics. Although there are some similarities between Chinese and Korean languages, there are still great differences in cultural background, pronunciation and grammar. This paper aims to discuss the principles of translating Korean lyrics from Chinese by analyzing the current situation, influencing factors and concrete examples.

Quantifying the impact of homophily and influencer networks on song popularity prediction.

Forecasting the popularity of new songs has become a standard practice in the music industry and provides a comparative advantage for those that do it well. Considerable efforts were put into machine learning prediction models for that purpose. It is known that in these models, relevant predictive parameters include intrinsic lyrical and acoustic characteristics, extrinsic factors (e.g., publisher influence and support), and the previous popularity of the artists. Much less attention was given to the social components of the spreading of song popularity. Recently, evidence for musical homophily-the tendency that people who are socially linked also share musical tastes-was reported. Here we determine how musical homophily can be used to predict song popularity. The study is based on an extensive dataset from the last.fm online music platform from which we can extract social links between listeners and their listening patterns. To quantify the importance of networks in the spreading of songs that eventually determines their popularity, we use musical homophily to design a predictive influence parameter and show that its inclusion in state-of-the-art machine learning models enhances predictions of song popularity. The influence parameter improves the prediction precision (TP/(TP FP)) by about 50% from 0.14 to 0.21, indicating that the social component in the spreading of music plays at least as significant a role as the artist's popularity or the impact of the genre.

‘It's mostly an accompaniment to something’

‘It's mostly an accompaniment to something’

Use Intention of Mobile Music and Audio Platforms Among Middle-Aged and Older Chinese Adults

Listening to music and audio is a main entertainment channel for middle-aged and older groups. Based on the technology acceptance model, we conducted interviews with 10 Chinese adults (M = 58.8, SD = 7.41) and a questionnaire with 355 users (M = 57.5, SD = 1.77) aged between 45 and 80 to explore the factors influencing the intention to use mobile music and audio platforms. The results reveal that perceived ease of use, social influence, satisfaction, habit, content quality, and perceived entertainment positively affect use intention, of which perceived ease of use is the most important. In addition, there is no significant difference in use intention between female and male users. Finally, we discuss the theoretical and practical implications of our findings. This study concludes six critical factors affecting the intention to use music platforms for Chinese middle-aged and older people. Platform designers should attach importance to the ease of use of platforms to make them more acceptable to middle-aged and older users.

Research on Product Competitiveness of the Digital Music Platform: A Case Study of the QQ Music Platform

The advent of the information age has led to a gradual transition of the traditional music industry towards digital development. This report delves into product innovation strategies implemented by QQ Music, such as its copyright strategy and product features, in response to intense competition within the digital music market. Based on the key findings, this report discusses how QQ Music’s innovative strategies have contributed to reinforcing the company’s dominance within China’s music streaming industry. A thorough SWOT analysis also serves as a strategic compass, highlighting the ways in which QQ Music strategically builds on its innate strengths and aggressively addresses its shortcomings. With a forward-looking perspective, this report provides insights into the future trajectory of QQ Music. The aim is to evolve strategies in response to changing copyright policies and to increase market competition. This research demonstrates QQ Music’s current product competitiveness and serves as a strategic roadmap for the platform’s future endeavors. Through constant strategy adjustments and grappling with the industry trend, QQ Music is positioned to both navigate and actively shape the changing digital music streaming marketplace in China.

How Technology Changes Monetization of Musical Digital Assets

This thesis examines how blockchain technology can affect the music business, as well as its potential applications and benefits over current practices for investors, users, and artists. It explores the music industry's current ecosystem and motivations for changes, and discusses the benefits and difficulties of using blockchain technology in a decentralized music platform (Web3). The thesis also demonstrates the mechanisms that enable these platforms to be built and run. Overall, the thesis emphasizes how blockchain technology has the ability to allow musicians more freedom, transparency, and flexibility when it comes to monetizing their music with less involvement of a third party.

Brand on the run: place brands as judgement devices and sources of local advantage in the music industry

ABSTRACT Products from certain areas are assumed to share qualities by virtue of where they are made. This article considers the economic significance of such place brands in the wider market for symbolic goods. It forwards a theory of these as judgement devices, whereby place reputation serves to lower search costs in symbolic goods markets with excess supply. This theory is investigated through a study of an online music platform where a weak form of place branding is available to producers. Results suggest that branding is associated with musical success at the individual level and that place brands may act as strategic resources for producers from creative clusters. Branding effects do not necessarily depend on the content of the place brand signal (i.e., country acts branded from Nashville are not especially privileged) and may be based in simpler heuristic mechanisms where a listed origin is a stamp of quality or an aid in recognition.

Movie and Music Recommendation System based on Facial Expressions

Abstract: This paper depicts a novel approach of recommendation system created to extract user’s facial expressions and analyze it using Convolutional Neural Network. CNN is a type of artificial neural network which primarily is used for image recognition and processing by recognizing patterns in images. Initially, an inbuilt webcam will operate to detect user’s facial expression which reduces system’s cost compared to alternative methods. The main concern in existing recommendation systems is manual sorting, so to avoid it, this model is proposed. ‘Movie and Music Recommendation System based on Facial expressions’ provides a way to automatically play the music and movie without spending much time browsing it. The most common problem we all face is, whenever we hop onto third party movies and music platforms, we find tons of options and get stuck into the confusion of what to watch in terms of movies and spend half of our time surfing the options and then we decide to watch something specific. Computer vision is used to understand people in images and videos. This research project introduces an ingenious recommendation system that capitalizes on facial expressions in real time, customize movie and music suggestions through emotion analysis. Haar Cascade algorithm and CNN targets to detect facial feature detection. The database of movies is routed to IMDB site and songs to Spotify API. Training of dataset is done on FER 2013 sample images. It has an input layer, convolutional layer, dense layer and output layer. After the emotions are detected, mood classification is done (for song: tone, valence, timber; for movie: happy, surprise, sad, neutral, anger.) The model is displayed on a website created using Html and CSS with Flask as interface. This system yields better performance results. This recommendation system is exhibited on a website which is user friendly created with HTML and CSS with Flask.

Using Neural and Graph Neural Recommender Systems to Overcome Choice Overload: Evidence From a Music Education Platform

The application of recommendation technologies has been crucial in the promotion of physical and digital content across numerous global platforms such as Amazon, Apple, and Netflix. Our study aims to investigate the advantages of employing recommendation technologies on educational platforms, with a particular focus on an educational platform for learning and practicing music. Our research is based on data from Tomplay, a music platform that offers sheet music with professional audio recordings, enabling users to discover and practice music content at varying levels of difficulty. Through our analysis, we emphasize the distinct interaction patterns on educational platforms like Tomplay, which we compare with other commonly used recommendation datasets. We find that interactions are comparatively sparse on educational platforms, with users often focusing on specific content as they learn, rather than interacting with a broader range of material. Therefore, our primary goal is to address the issue of data sparsity. We achieve this through entity resolution principles and propose a neural network (NN)-based recommendation model. Further, we improve this model by utilizing graph neural networks (GNNs), which provide superior predictive accuracy compared to NNs. Notably, our study demonstrates that GNNs are highly effective even for users with little or no historical preferences (cold-start problem). Our cold-start experiments also provide valuable insights into an independent issue, namely, the number of historical interactions needed by a recommendation model to gain a comprehensive understanding of a user. Our findings demonstrate that a platform acquires a solid knowledge of a user’s general preferences and characteristics with 50 past interactions. Overall, our study makes significant contributions to information systems research on business analytics and prescriptive analytics. Moreover, our framework and evaluation results offer implications for various stakeholders, including online educational institutions, education policymakers, and learning platform users.

Copyright and digital music platforms: harmony or dissonance?

This study addresses the complex relationship between copyright and digital music platforms in the digital era. As digital platforms become the primary means of music distribution, significant challenges arise concerning the protection and fair compensation of authors. The article explores the evolution of the music industry, from the early days of the phonograph to modern streaming platforms, highlighting the technological changes that have shaped the relationship between music and technology. The copyright system in Brazil is also discussed, influenced by the French dual protection model, encompassing both economic and moral rights. The text also analyzes the role of the Central Office for Collection of Copyright (ECAD) and its seven Associations in the collective management of copyright, as well as the issue of transparency in the distribution of economic rights. One point of debate is whether music transmission through streaming platforms should be considered public or private performance, directly impacting copyright revenue collection. Therefore, this study aims to present the relevant panorama of this new context of copyright associated with digital music platforms (streaming). Adopting a deductive methodology with exploratory research, the article concludes that the landscape of copyright in Brazil and its relationship with digital music platforms is a complex and challenging topic that requires careful consideration and appropriate regulation to ensure authors are adequately protected in a constantly evolving digital environment. It emphasizes the need for greater transparency and clarity in the collection, calculation, and distribution system of economic rights by digital music platforms.