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Hit or miss: A decision support system framework for signing new musical talent

Author

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  • Choicharoon, Aritad
  • Hodgett, Richard
  • Summers, Barbara
  • Siraj, Sajid

Abstract

In the music industry, the process of signing new musical talent is one of the most complex decision-making problems. The decision, which is generally made by an artist and repertoire (A&R) team, involves consideration of various quantitative and qualitative criteria, and usually results in a low success rate. We conducted a series of mental model interviews with the aim of developing a decision support framework for A&R teams. This framework was validated by creating a decision support system that utilises multi-criteria decision analysis to support decision-making. Our framework and subsequent implementation of the decision support system involving decision rule and weighted sum methods show an improvement in the ability to analyse and decide on greater amounts of talent. This paper serves as a building block for developing systems to aid in this complex decision-making problem.

Suggested Citation

  • Choicharoon, Aritad & Hodgett, Richard & Summers, Barbara & Siraj, Sajid, 2024. "Hit or miss: A decision support system framework for signing new musical talent," European Journal of Operational Research, Elsevier, vol. 312(1), pages 324-337.
    • Handle: RePEc:eee:ejores:v:312:y:2024:i:1:p:324-337
    DOI: 10.1016/j.ejor.2023.06.014
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    References listed on IDEAS

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    1. Bender, Mark & Gal-Or, Esther & Geylani, Tansev, 2021. "Attracting artists to music streaming platforms," European Journal of Operational Research, Elsevier, vol. 290(3), pages 1083-1097.
    2. Zhang, Ruijun & Lu, Jie & Zhang, Guangquan, 2011. "A knowledge-based multi-role decision support system for ore blending cost optimization of blast furnaces," European Journal of Operational Research, Elsevier, vol. 215(1), pages 194-203, November.
    3. Quintero, Alejandro & Konare, Dougoukolo & Pierre, Samuel, 2005. "Prototyping an intelligent decision support system for improving urban infrastructures management," European Journal of Operational Research, Elsevier, vol. 162(3), pages 654-672, May.
    4. Helmut Jungermann & Holger Schütz & Manfred Thüring, 1988. "Mental Models in Risk Assessment: Informing People About Drugs," Risk Analysis, John Wiley & Sons, vol. 8(1), pages 147-155, March.
    5. Arno de Caigny & Kristof Coussement & Koen W. de Bock, 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," Post-Print hal-01741661, HAL.
    6. Haastrup, P. & Maniezzo, V. & Mattarelli, M. & Mazzeo Rinaldi, F. & Mendes, I. & Paruccini, M., 1998. "A decision support system for urban waste management," European Journal of Operational Research, Elsevier, vol. 109(2), pages 330-341, September.
    7. Steininger, Dennis M. & Gatzemeier, Simon, 2019. "Digitally forecasting new music product success via active crowdsourcing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 167-180.
    8. Leonard, Kevin J., 1995. "The development of a rule based expert system model for fraud alert in consumer credit," European Journal of Operational Research, Elsevier, vol. 80(2), pages 350-356, January.
    9. Eric Strobl & Clive Tucker, 2000. "The Dynamics of Chart Success in the U.K. Pre-Recorded Popular Music Industry," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 24(2), pages 113-134, May.
    10. Vaidya, Omkarprasad S. & Kumar, Sushil, 2006. "Analytic hierarchy process: An overview of applications," European Journal of Operational Research, Elsevier, vol. 169(1), pages 1-29, February.
    11. Arampatzis, G. & Kiranoudis, C. T. & Scaloubacas, P. & Assimacopoulos, D., 2004. "A GIS-based decision support system for planning urban transportation policies," European Journal of Operational Research, Elsevier, vol. 152(2), pages 465-475, January.
    12. HERREMANS, Dorien & MARTENS, David & SÖRENSEN, Kenneth, 2014. "Dance hit song prediction," Working Papers 2014003, University of Antwerp, Faculty of Business and Economics.
    13. Figueira, Jose & Roy, Bernard, 2002. "Determining the weights of criteria in the ELECTRE type methods with a revised Simos' procedure," European Journal of Operational Research, Elsevier, vol. 139(2), pages 317-326, June.
    14. De Caigny, Arno & Coussement, Kristof & De Bock, Koen W., 2018. "A new hybrid classification algorithm for customer churn prediction based on logistic regression and decision trees," European Journal of Operational Research, Elsevier, vol. 269(2), pages 760-772.
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