Abstract The emergence of new technologies such as mp3 and music streaming, and the accompanying digital transformation of the music industry, have led to the shift and change of the entire music industry’s value chain. While music is increasingly being consumed through digital channels, the number of empirical studies, particularly in the field of music copyright in the digital music industry, is limited. Every year, rightsholders of musical works, valued 2.5 billion dollars, remain unknown. The objectives of this study are twofold: First to understand and describe the structure and process of the Dutch music copyright system including the most relevant actors within the system and their relations. Second to apply evolutionary economics approach and Values Sensitive Design method within the context of music copyright through positive-empirical perspective. For studies of technological change in existing markets, the evolutionary economics literature provides a coherent and evidence-based foundation. The actors are generally perceived as being different, for example with regard to their access to information, their ability to handle information, their capital and knowledge base (asymmetric information). Also their norms, values and roles can differ. Based on an analysis of documents and held expert interviews, we find that the collection and distribution of the music copyright money is still based on obsolete laws, neoclassical paradigm and legacy IT-system. Finally, we conclude that the rightsholders are heterogenous and have asymmetrical information and negotiating power. The outcomes of this study contribute to create a better understanding of impact of digitization of music copyright industry and empower the stakeholders to proceed from a more informed perspective on redesigning and applying the future music copyright system and pre-digital norms and values amongst actors.
This paper aims to offer a critical reflection on the way Talent Management (TM) is investigated in practice, by addressing the key issues regarding the quality (in terms of rigor and relevance) of academic empirical TM research and therefore the critical scrutiny of TM scholars’ work. We will argue that despite the growth in the quantity, the quality of many empirical TM papers is lagging behind and hindering the progress of the academic field of TM.
Algorithmic curation is a helpful solution for the massive amount of content on the web. The term is used to describe how platforms automate the recommendation of content to users. News outlets, social networks and search engines widely use recommendation systems. Such automation has led to worries about selective exposure and its side effects. Echo chambers occur when we are over-exposed to the news we like or agree with, distorting our perception of reality (1). Filter bubbles arise where the information we dislike or disagree with is automatically filtered out – narrowing what we know (2). While the idea of Filter Bubbles makes logical sense, the magnitude of the "filter bubble effect", reducing diversity, has been questioned [3]. Most empirical studies indicate that algorithmic recommendations have not locked large audience segments into bubbles [4]. However, little attention has been paid to the interplay between technological, social, and cognitive filters. We proposed an Agent-based Modelling to simulate users' emergent behaviour and track their opinions when getting news from news outlets and social networks. The model aims to understand under which circumstances algorithmic filtering and social network dynamics affect users' innate opinions and which interventions can mitigate the effect. Agent-based models simulate the behaviour of multiple individual agents forming a larger society. The behaviour of the individual agents can be elementary, yet the population's behaviour can be much more than the sum of its parts. We have designed different scenarios to analyse the contributing factors to the emergence of filter bubbles. It includes different recommendation algorithms and social network dynamics. Cognitive filters are based on the Triple Filter Bubble model [5].References1.Richard Fletcher, 20202.Eli Pariser, 20123.Chitra & Musco, 20204. Möller et al., 20185. Daniel Geschke et al, 2018
