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Fundraising, social media and tourism in American symphony orchestras and opera houses

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Abstract

The multiple segmentation of audiences and stakeholders is as essential to the survival of American symphony orchestras and opera houses as is revenue diversification. Fundraising remains the key source of revenue, but managers are now exploring new target audiences with the increased use of non-traditional social media. Marketing towards non-traditional audiences such as musical tourists is coming under increasing scrutiny. Using a normal mixture cluster analysis of revenues and expenses in 2015, and considering a cultural tourism ratio, economic performances and targets of social media, this paper will segment a sample of some of the largest symphony orchestras and opera houses in the United States.

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Notes

  1. Rosen (2017), Ross (2017), Pompe and Tamburi (2016), Bagwell et al. (2015), Besana (2012), Ravanas (2007, 2008), Smith (2007) and Borgonovi (2006).

  2. Tamburri et al. (2015), Song and Yi (2011) and Ravanas (2008).

  3. Lee et al. (2015), Liu (2014), Aguiar-Quintana (2015), Gligorijevic (2014) and Guachalla (2012).

  4. Voss et al. (2016), Cancellieri and Turrini (2016), Kemp and Poole (2016) and Besana (2012).

  5. Besana et al. (2018), Esposito (2016), Kemp and Poole (2016), Kotler and Armstrong (2012). Turbide and Laurin (2009) and Radbourne et al. (2009).

  6. Normal Mixtures clustering is useful when data come from overlapping normal distributions. Normal mixtures is an iterative technique based on the assumption that the joint probability distribution of the observations is approximated using a mixture of multivariate normal distributions. These mixtures represent different clusters. The individual clusters have multivariate normal distributions. When clusters are well separated, hierarchical and k-means clustering work well. But when clusters overlap, normal mixtures provide a better alternative, because it is based on cluster membership probabilities, rather than arbitrary cluster assignments based on borders.

  7. Lee et al. (2015), Liu (2014), Aguiar-Quintana (2015), Gligorijevic (2014), Cohen (2013), Guachalla (2012), Boerner et al. (2011), Woosnam et al. (2009) and Poon and Lai (2008).

  8. Kemp and Poole (2016), Stelzner (2015), Moretti and Tuan (2014), Hong (2014), Pierotti et al. (2014), Nadeem (2012), Kietzmann et al. (2011), Yi (2010), League of American Orchestras (2009) and Neslin and Shakar (2009).

  9. Topper (2007).

  10. Wroblewski (2017).

  11. Waters (2010) and Rogers (1983).

  12. Guo and Saxton (2014), Lovejoy and Saxton (2012), Nah and Saxton (2012) and Waters (2007).

  13. Enright and Bourns (2010), Kaplan and Haenlein (2010), Lovejoy and Saxton (2012), Nah and Saxton (2012) and Macnamara and Zerfass (2012).

  14. Jo and Kim (2003).

  15. O’Neil (2014) and Saxton and Wang (2013).

  16. Saxton and Wang (2013).

  17. Conti and Carriero (2014).

  18. Waters et al. (2009) and Waters and Lo (2012).

  19. Waters and Jamal (2011).

  20. Kanter and Fine (2010).

  21. Roberts (2014).

  22. Zickuhr and Madden (2012).

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  1. IULM University, Milan, Italy

    Angela Besana & Annamaria Esposito

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  1. Angela Besana
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  2. Annamaria Esposito
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Correspondence to Angela Besana.

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Besana, A., Esposito, A. Fundraising, social media and tourism in American symphony orchestras and opera houses. Bus Econ 54, 137–144 (2019). https://doi.org/10.1057/s11369-019-00118-7

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