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Information Retrieval Technologies and Big Data Analytics to Analyze Product Innovation in the Music Industry

Author

Listed:
  • Michele Gorgoglione

    (Department of Mechanics, Mathematics and Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy)

  • Achille Claudio Garavelli

    (Department of Mechanics, Mathematics and Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy)

  • Umberto Panniello

    (Department of Mechanics, Mathematics and Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy)

  • Angelo Natalicchio

    (Department of Mechanics, Mathematics and Management, Politecnico di Bari, Via Orabona 4, 70125 Bari, Italy)

Abstract

In Cultural and Creative Industries, innovation contributes to generating a competitive advantage thanks to the fundamental role assumed by the human creativity and the quest for novelty. In particular, the music industry stands out as one of the most successful ones, in terms of both revenue and employment. The music industry is also quickly and constantly growing, supported by the new digital technologies and the rise of streaming platforms and digital services, which have increased the availability of continuous, reliable, and timely data. Consequently, this may allow the implementation of novel techniques to study product innovation occurring in the music industry. Nonetheless, quantitative approaches to study innovation in this industry are scant. The present study aims at filling this gap by developing a quantitative approach to analyze product innovation dynamics in the music industry exploiting data collected through Music Information Retrieval technologies. We selected a successful band as a case study and analyzed each song released from 1984 to 2016 to obtain a quantitative representation of their musical production. We then developed and applied quantitative similarity metrics to see how each album was similar or different from the previous ones and from the most relevant music genres, to better understand innovation dynamics in music production.

Suggested Citation

  • Michele Gorgoglione & Achille Claudio Garavelli & Umberto Panniello & Angelo Natalicchio, 2023. "Information Retrieval Technologies and Big Data Analytics to Analyze Product Innovation in the Music Industry," Sustainability, MDPI, vol. 15(1), pages 1-16, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:828-:d:1023169
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    References listed on IDEAS

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