Using The Wisdom of the Crowd to Predict Popular Music Chart Success


Steininger, Dennis M. ; Gatzemeier, Simon


URL: http://aisel.aisnet.org/ecis2013_cr/215/
Additional URL: http://aisel.aisnet.org/cgi/viewcontent.cgi?articl...
Document Type: Conference or workshop publication
Year of publication: 2013
Book title: ECIS 2013 Proceedings : 21st European Conference on Information Systems June 5-8, Utrecht University, The Netherlands
Page range: Paper 215
Date of the conference: 06.-08.06.2013
Place of publication: Atlanta, Ga.
Publishing house: AISeL
Publication language: English
Institution: Außerfakultäre Einrichtungen > Institut für Mittelstandsforschung (ifm)
Business School > Mittelstandsforschung u. Entrepreneurship (Woywode)
Business School > Dieter-Schwarz-Stiftungslehrstuhl für ABWL, E-Business u. E-Government (Veit -2013)
Subject: 650 Management
Abstract: The peculiarities of the recording industry system, such as fashion cycles, the hedonic nature of music, socio-network effects, informal heuristics in the decision-making process of recording companies, and the opaque selection process of media gatekeepers have created uncertainty about the chart potential of musical products. With respect to the on-going digital transformation and shift in power from organizations to consumers, we leverage the principles of crowdsourcing to build a prediction model for understanding chart success. Therefore, we investigate the causal relationship between crowd evaluations based on listening experience and popular music chart success. We use 150 music songs and track their live cycles with entry and peak positions in reported music charts. Additionally, we carry out about 20 evaluations by the crowd for each song resulting in a total of 2.852 observations. Our findings indicate that the crowd has predictive relevance concerning popular music chart success. However, this predictive relevance is bound to certain conditions, namely the composition of the crowd, the underlying chart and market mechanisms and the novelty of the musical material. In sum we find that crowd-based mechanisms are especially suited for predicting the performance of novel songs from unknown artists, which makes them a powerful decision support instrument in very uncertain contexts with limited historical data availability. mited historical data availability.

Dieser Eintrag ist Teil der Universitätsbibliographie.




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Steininger, Dennis M. ; Gatzemeier, Simon Using The Wisdom of the Crowd to Predict Popular Music Chart Success. Paper 215 In: ECIS 2013 Proceedings : 21st European Conference on Information Systems June 5-8, Utrecht University, The Netherlands (2013) Atlanta, Ga. [Conference or workshop publication]


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