The Sound of Progress: Investigating the Representation of Artificial Intelligence in Music

2023 | International Conference on Business Informatics | Citations: 0

Authors: Banh, Leonardo; Strobel, Gero

Abstract: The role of artificial intelligence in daily life is constantly advancing and h ... Expand

Abstract: The role of artificial intelligence in daily life is constantly advancing and has become an important topic of discussion. With music and its lyrics being a vehicle to express topics of society, this paper investigates how artificial intelligence is perceived by musicians and reflected in their songs. By analyzing the lyrics of over 1,200 songs over three decades, this work applies sentiment analysis to extract polarities and emotions. The results provide insights into how musicians view and reflect the impact of artificial intelligence on society and how this is reflected in their song texts. The findings show an increase in songs mentioning artificial intelligence-related terms, with a trend of more songs implying negativity, such as anger. However, minor increases in positive emotions indicate musicians' ambivalent views on hopes and fear of artificial intelligence. Collapse

Topics: artificial intelligence deepfake database system

Methods: sentiment analysis deep learning machine learning language model simple linear regression

Theories: communication theory

Blockchain-based digital rights management systems: Design principles for the music industry

2023 | Electronic Markets | Citations: 0

Authors: Ciriello, Raffaele Fabio; Torbensen, Alexandra Cecilie Gjøl; Hansen, Magnus Rotvit Perlt; Müller-Bloch, Christoph

Abstract: Initially designed to protect intellectual property (IP) of digitalized informat ... Expand

Abstract: Initially designed to protect intellectual property (IP) of digitalized information goods such as music, games, or books, existing centralized digital rights management (DRM) systems mostly serve the interests of major publishers, with scant inclusion of rights owners, creators, and consumers. Although various blockchain-based DRM systems have been proposed, most of them mirror existing counterproductive IP restrictions. Analyzing the music industry as a case in point, this paper proposes design principles for blockchain-based DRM systems that provide an integrated and flexible solution by enabling transparent music licensing structures, consistent and complete rights metadata, and efficient and transparent royalty payout. The solution can be achieved by storing rights metadata on a public distributed ledger, by validating metadata through a consensus mechanism on a permissioned blockchain, and by algorithmically enforcing royalty payout via stablecoin through a smart contract. The design principles were evaluated by industry experts, validating their benefit for the music industry by increasing surplus value that is currently destroyed through previous suboptimal designs. Collapse

Topics: blockchain stablecoin smart contract distributed ledger technology cryptocurrency

Methods: design principle design requirement qualitative interview design artifact design science

Theories: transaction cost economics socio technical theory

Reputation Spillover from Agencies on Online Platforms: Evidence from the Entertainment Industry

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Kokkodis, Marios; Adamopoulos, Panagiotis; Ransbotham, Sam

Abstract: Digital markets have proliferated in recent years, overcoming many market ineff ... Expand

Abstract: Digital markets have proliferated in recent years, overcoming many market inefficiencies by facilitating direct interactions between consumers and creators. Thanks to this disintermediation, consumers now have access to a vast number of alternatives, while creators can efficiently reach huge markets. However, the success of digital markets has created a concomitant challenge for creators: differentiation. In crowded markets, agencies (e.g., publishing companies in books, freelance agencies in online labor markets, independent labels in music) can differentiate creators by signaling product quality. But how do agencies' reputations affect product success for creators? Can some agencies do more harm than good? To investigate these research questions, we theorize how variation in creator and agency reputation leads to asymmetric and heterogeneous effects, namely that (1) more reputable agencies have a stronger positive effect on less reputable creators than they have on more reputable creators, and (2) less reputable agencies hurt more reputable creators more than they hurt less reputable ones. Analyses of more than one million observations from two digital markets provide empirical support for these theory-driven arguments. The findings have design implications for markets and contribute to our understanding of how agencies, depending on creator reputation, can either benefit or hurt product success.Marios Kokkodis is a machine learning scientist working on personalization, recommender systems, and markets. His work appears in major information systems, data mining, and multidisciplinary outlets. He has received multiple awards and nominations, including the INFORMS ISS Gordon B. Davis Young Scholar Award, the INFORMS ISS Nunamaker-Chen dissertation award, the NYU Stern Harold W. MacDowell award, and the LinkedIn economic graph challenge award. Collapse

Topics: electronic market social media Google+ digital platform marketing management

Methods: theory development econometric modeling descriptive statistic gradient boosting machine learning

Theories: cognitive theory expectation disconfirmation theory decision theory contrast theory assimilation theory of learning

Understanding Online Music Piracy Behavior via Private Communication Channels

2023 | Information Systems Frontiers | Citations: 0

Authors: Park, Soomin; Moon, Junghoon; Rhee, Cheul; Choe, Young-Chan

Abstract: In this study, we developed and tested a theoretical model to understand online ... Expand

Abstract: In this study, we developed and tested a theoretical model to understand online music piracy behavior via email and instant messaging. We developed this theoretical model by drawing on a comprehensive literature review on music piracy, theory of reasoned action, social identity, and the deterrence theory. We collected primary data to test the hypotheses, and the results from the partial least squares analyses suggest attitude toward music file-sharing behavior and social identity positively affect intention to pirate music, whereas the perceived severity and certainty of punishment do not have significant effects on the attitude. The findings highlight the important role of social identity when a theoretical model with the concept explains music file-sharing behavior without monetary benefits in return. Collapse

Topics: social identity digital piracy electronic mail social media online chat

Methods: partial least squares regression survey structural equation modeling survey design literature study

Theories: deterrence theory theory of reasoned action behavioral theory

Know Your Firm: Managing Social Media Engagement to Improve Firm Sales Performance

2023 | Management Information Systems Quarterly | Citations: 0

Authors: Ren, Fei; Tan, Yong; Wan, Fei

Abstract: We examine the impact of firm social media engagement on sales performance, ans ... Expand

Abstract: We examine the impact of firm social media engagement on sales performance, answering "whether," "what," and "how" questions. The study uses a quasi-experimental design in a social e-commerce setting, for which propensity score matching and difference-in-differences methods quantify a mean 20.67% sales increase after firm social media adoption. We also find that firms that sell low-involvement products benefit more from social media adoption, compared to those that sell high-involvement products. Further, in terms of how to manage social media engagement, we find that informative content, in general, is effective for sales of high-involvement products, whereas promotional content, a new type of content discovered in this study, is more beneficial for sales of low-involvement products. Meanwhile, more social media followers generate better firm sales performance. We used instrumental variables and the control function method to address endogeneity issues and conducted robustness checks to support our conclusions. This study sheds light on the value of firm social media, particularly regarding industry differences and firm know-how. Collapse

Topics: social media blog electronic commerce decision making organizational productivity

Methods: experimental group instrumental variables estimation propensity score method qualitative content analysis computational algorithm

Theories: social presence theory communication theory

A collaborative filtering model incorporating media promotions and users' variety-seeking tendencies in the digital music market

2023 | Decision Support Systems | Citations: 0

Authors: Lee, Myounggu; Kim, Hye-Jin

Abstract: Understanding customer preferences and providing the right products at the right ... Expand

Abstract: Understanding customer preferences and providing the right products at the right time to customers via personalized recommendations have been among the major interests of online retailers and service providers. This paper proposes an improved collaborative filtering model that incorporates a firm's marketing effort vari­ ables (i.e., media promotional variables) to improve the prediction of customers' digital music choices. In addition, we assert that the predictive model's effectiveness is different for consumers depending on their varietyseeking tendencies in music. We compared our predictive model to benchmark models and demonstrated that our proposed model is superior in predicting users' download behavior. We also found that the overall predictive performance is higher for active variety seekers who consume diverse types of music via streaming. We provide some evidence that this may be due to differences in the degree to which the two groups are influenced by different types of media promotions. The results suggest that considering psychological characteristics such as variety-seeking tendencies provides more advantages in prediction and recommendation systems, which opens new avenues for improvement. Collapse

Topics: collaborative filtering YouTube marketing management music download social media

Methods: experiment computational algorithm random forest classification regression analysis method deep learning

Pay-What-You-Want Pricing in the Digital Product Marketplace: A Feasible Alternative to Piracy Prevention?

2022 | Information Systems Research | Citations: 0

Authors: Kim, Byung Cho; Park, So Eun; Straub, Detmar W.

Abstract: Pay-what-you-want (PWYW) pricing is a pricing scheme under which buyers pay any ... Expand

Abstract: Pay-what-you-want (PWYW) pricing is a pricing scheme under which buyers pay any amount for a product, often including zero. Digital product firms may particularly benefit from PWYW pricing because it can be utilized as an alternative to their costly piracy prevention efforts. In this paper, we examine PWYW pricing based on the established social preference theory where consumers may be concerned with how fairly they are being treated by firms. Utilizing a two-segment model for consumers (self-interested versus fair minded), we study PWYW pricing against traditional, posted pricing in a monopolistic digital product market. We show that under posted pricing, it is optimal to eliminate piracy when the quality development cost is sufficiently high. Interestingly, high quality of the illegal copy associated with low quality development cost makes PWYW pricing an effective alternative to posted pricing. We also examine the impact of network externalities on PWYW pricing. Counterintuitively, despite the full market penetration, PWYW pricing invariably becomes less profitable than posted pricing as network externalities become sufficiently strong. Our findings explain why PWYW pricing is rare in the established digital product marketplace with strong network externalities despite near-zero marginal costs. Collapse

Topics: price management network effect digital product digital piracy intellectual property

Methods: literature sample simulation

Dancing to the #challenge: The Effect of TikTok on Closing the Artist Gender Gap

2022 | International Conference on Information Systems | Citations: 0

Authors: Wang, Yifei; Ramaprasad, Jui; Gopal, Anand

Abstract: This study examines how an interesting technological phenomenon called the "Has ... Expand

Abstract: This study examines how an interesting technological phenomenon called the "Hashtag Dance Challenge" (HDC), made popular on the short video platform TikTok, may drive artists' popularity on the digital music streaming platform, and, importantly, help women, artists, to achieve traction that is needed to succeed commercially. Using data from TikTok, Spotify, and music analytics companies, we analyze the impact of HDCs on artists' popularity growth rate on Spotify. We find that artists with an HDC-related song achieve a significant daily increase in followership on Spotify, representing traction and appeal within the music industry, relative to similar artists who do not have an HDC. Importantly, the daily growth of Spotify followers increases by approximately 3% more for female artists than male artists, given an HDC-associated song. Our findings shed new light on the role of social media with respect to artist self-promotion, especially in making the music more inclusive and attractive to female music artists. Collapse

Topics: TikTok digital service Spotify music streaming service social media

Methods: experimental group propensity score matching propensity score method logistic regression longitudinal research

Opening the Black Box of Music Royalties with the Help of Hybrid Intelligence

2021 | HICSS | Citations: 1

Authors: Elshan, Edona; Engel, Christian; Ebel, Philipp

Abstract: The ever-increasing complexity of the music industry and the intensified resentm ... Expand

Abstract: The ever-increasing complexity of the music industry and the intensified resentment of artists towards collecting societies call for a transformation and a change of behavior within the music ecosystem. This article introduces a hybrid intelligence system, that ameliorates the current situation by combining the intelligence of humans and machines. This study proposes design requirements for hybrid intelligence systems in the music industry. Using a design science research approach, we identify design requirements both inductively from expert interviews and deductively from theory and present a first prototypical instantiation of a respective hybrid intelligence system. Overall, this shall enrich the body of knowledge of hybrid intelligence research by transferring its concepts into a new context. Furthermore, the identified design requirements shall serve as a foundation for researchers and practitioners to further explore and design hybrid intelligence in the music industry and beyond. Collapse

Topics: digital transformation video conference database system value creation

Methods: design requirement qualitative interview literature study design science design artifact

Music intelligence: Granular data and prediction of top ten hit songs

2021 | Decision Support Systems | Citations: 1

Authors: Kim, Seon Tae; Oh, Joo Hee

Abstract: In the music market, superstars significantly dominate the market share, while p ... Expand

Abstract: In the music market, superstars significantly dominate the market share, while predicting the top hit songs is notoriously difficult. The music intelligence technology, retrieving and utilizing granular acoustic features of songs, provides opportunities to improve the prediction of top hit songs. Using data on 6209 unique songs that appeared in the weekly Billboard Hot 100 charts from 1998 to 2016, especially acoustic features provided by Spotify, we investigate empirically how the top-10-hit-songs likelihood prediction is improved by acoustic fea­ tures. We find that some acoustic features (e.g., danceability, happiness, and some metrics of timbre and pitch) significantly improve the model’s ability to predict the top-10-hit-songs probability. These results suggest that the granular data, provided by the music intelligence technology, carries a substantial predictive value in the era of online music streaming. Collapse

Topics: digital service Spotify music streaming service logistics management internet technology

Methods: computational algorithm machine learning descriptive statistic generalized linear model McNemar's test