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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