پیش بینی دیجیتال موفقیت محصول جدید
Abstract
1. Introduction
2. Theoretical foundations and literature review
3. Method
4. Data analysis and estimation results
5. Discussion and implications
6. Limitations and future research
7. Conclusion
Appendix A
References
Abstract
Deciding which artist or song to sign and promote has always been a challenge for recording companies, especially when it comes to innovative newcomer singers without any chart history. However, the specifics of a creative industry such as the hedonic nature of music, socio-network effects, and ever fastening fashion cycles in combination with digitalization have made the recording industry even more competitive and these initial decisions even more crucial. With respect to the ongoing digital transformation and shift in power from organizations to consumers, we leverage digitally mediated wisdom of the crowd to build a forecasting model for better understanding chart success. Therefore, we draw on the literature of hedonic and experiential goods to investigate the relationship between crowd evaluations based on listening experience and popular music chart success. We track 150 song positions in reported music charts and also evaluate these songs via the crowd. Our model indicates that the wisdom of the crowd can improve forecasting chart success by almost 30% relatively to factors that have been earlier identified in the literature. However, this forecasting 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 to forecast the performance of novel songs from unknown artists, which makes them a powerful yet very affordable decision support instrument for very uncertain contexts with limited historical data available. These findings can support recording companies to address the challenge of signing newcomers and thereby further enable the innovation system of the industry.
Introduction
It has always been crucial for music companies and labels to sign the right newcomer artists and select the best songs for a release in order to ensure a constant revenue stream from music product sales (Cameron, 2016; Dewan and Ramaprasad, 2014; Ordanini, 2006). These decisions are by no way perfect even today, which is reflected in revenues that are distributed according to the power law: revenues of few commercial successes compensate for the losses of many failures (Cameron, 2016; Strobl and Tucker, 2000). The reasons for this high number of failures and their challenges for firm survival can be found in the complex and uncertain market characteristics of the music publishing industry as well as in ever changing technologies. First, the consequences of the digital transformation, such as music piracy, lower per unit revenues of digital content, and new business models have diminished the total profitability of the industry (Aguiar and Waldfogel, 2015; Benner and Waldfogel, 2016; Dobusch and Schüßler, 2014; Hiller, 2016; Lam and Tan, 2001). Second, the selection criteria of the recording companies are based on informal heuristics since the hedonic nature of music prevents an objective measurement of its quality. Third, direct and large-scale consumer engagement has been expensive. In order to increase the chances of reaching the consumer, the products are therefore developed and bundled according to the expected consumer taste formulated by the media gatekeepers (Hirsch, 1972; Ordanini, 2006). Fourth, consumer taste is susceptible to fashion cycles, socio-network effects and decision difficulties due to increasing choice of consumption, which result in diversity and uncertainty about expected demand.