IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i1p828-d1023169.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/828/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/828/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mariani, Marcello M. & Fosso Wamba, Samuel, 2020. "Exploring how consumer goods companies innovate in the digital age: The role of big data analytics companies," Journal of Business Research, Elsevier, vol. 121(C), pages 338-352.
    2. Xu Chen & Chunhong Liu & Yao Jiang & Changchun Gao, 2021. "What Causes the Virtual Agglomeration of Creative Industries?," Sustainability, MDPI, vol. 13(16), pages 1-18, August.
    3. Grupp, Hariolf, 1994. "The measurement of technical performance of innovations by technometrics and its impact on established technology indicators," Research Policy, Elsevier, vol. 23(2), pages 175-193, March.
    4. Mariani, Marcello M. & Nambisan, Satish, 2021. "Innovation Analytics and Digital Innovation Experimentation: The Rise of Research-driven Online Review Platforms," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    5. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.
    6. Kathy A. Paulson Gjerde & Susan A. Slotnick & Matthew J. Sobel, 2002. "New Product Innovation with Multiple Features and Technology Constraints," Management Science, INFORMS, vol. 48(10), pages 1268-1284, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mariani, Marcello M. & Machado, Isa & Nambisan, Satish, 2023. "Types of innovation and artificial intelligence: A systematic quantitative literature review and research agenda," Journal of Business Research, Elsevier, vol. 155(PB).
    2. Boccali, Filippo & Mariani, Marcello M. & Visani, Franco & Mora-Cruz, Alexandra, 2022. "Innovative value-based price assessment in data-rich environments: Leveraging online review analytics through Data Envelopment Analysis to empower managers and entrepreneurs," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    3. Chatterjee, Sheshadri & Mariani, Marcello & Fosso Wamba, Samuel, 2023. "Prosumers’ intention to co-create business value and the moderating role of digital media usage," Journal of Business Research, Elsevier, vol. 163(C).
    4. Saura, Jose Ramon & Ribeiro-Soriano, Domingo & Zegarra Saldaña, Pablo, 2022. "Exploring the challenges of remote work on Twitter users' sentiments: From digital technology development to a post-pandemic era," Journal of Business Research, Elsevier, vol. 142(C), pages 242-254.
    5. Motamarri, Saradhi & Akter, Shahriar & Hossain, Md Afnan & Dwivedi, Yogesh K, 2022. "How does remote analytics empowerment capability payoff in the emerging industrial revolution?," Journal of Business Research, Elsevier, vol. 144(C), pages 1163-1174.
    6. Xie, Weihong & Zou, Yukun & Guo, Haizhen & Li, Zhongshun, 2024. "What drives digital innovation cycles? Evidence from manufacturing enterprises in China," Technological Forecasting and Social Change, Elsevier, vol. 204(C).
    7. Chatterjee, Sheshadri & Chaudhuri, Ranjan & Mariani, Marcello & Fosso Wamba, Samuel, 2023. "Examining the role of intellectual capital on knowledge sharing in digital platform-based MNEs and its impact on firm performance," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    8. Dal Mas, F. & Massaro, M. & Ndou, V. & Raguseo, E., 2023. "Blockchain technologies for sustainability in the agrifood sector: A literature review of academic research and business perspectives," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    9. Mariani, Marcello & Dwivedi, Yogesh K., 2024. "Generative artificial intelligence in innovation management: A preview of future research developments," Journal of Business Research, Elsevier, vol. 175(C).
    10. Borghi, Matteo & Mariani, Marcello M., 2022. "The role of emotions in the consumer meaning-making of interactions with social robots," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    11. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    12. Robert Stehrer & Julia Wörz, 2003. "Technological convergence and trade patterns," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 139(2), pages 191-219, June.
    13. Beschorner, Patrick Frank Ernst, 2008. "Do Shorter Product Cycles Induce Patent Thickets?," ZEW Discussion Papers 08-098, ZEW - Leibniz Centre for European Economic Research.
    14. Blind, Knut & Grupp, Hariolf, 1999. "Interdependencies between the science and technology infrastructure and innovation activities in German regions: empirical findings and policy consequences," Research Policy, Elsevier, vol. 28(5), pages 451-468, June.
    15. Maniyassouwe Amana & Pingfeng Liu & Mona Alariqi, 2022. "Value Creation and Capture with Big Data in Smart Phones Companies," Sustainability, MDPI, vol. 14(23), pages 1-22, November.
    16. Fosso Wamba, Samuel & Queiroz, Maciel M. & Trinchera, Laura, 2024. "The role of artificial intelligence-enabled dynamic capability on environmental performance: The mediation effect of a data-driven culture in France and the USA," International Journal of Production Economics, Elsevier, vol. 268(C).
    17. Zhe OuYang & Peng Cheng & Yang Liu, 2020. "The role of product line breadth, product pre-entry experience, and market uncertainty in explaining followers’ speed of feature entry," Review of Managerial Science, Springer, vol. 14(6), pages 1221-1249, December.
    18. Keld Laursen, 1998. "Revealed Comparative Advantage and the Alternatives as Measures of International Specialisation," DRUID Working Papers 98-30, DRUID, Copenhagen Business School, Department of Industrial Economics and Strategy/Aalborg University, Department of Business Studies.
    19. Yuan, Sai & Zhou, Ran & Li, Mengna & Lv, Chengchao, 2023. "Investigating the influence of digital technology application on employee compensation," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
    20. Duysters, G. & Hagedoorn, J., 2000. "The effect of mergers and acquisitions on the technological performance of companies in a high-tech environment," Working Papers 00.04, Eindhoven Center for Innovation Studies.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:828-:d:1023169. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.