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The impact of social media activities on theater demand

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Abstract

A well-known factor in the consumption of cultural goods is that demand is subject to the ‘nobody knows’ principle and therefore difficult to predict. Other sectors have successfully analyzed social media data to predict real-world outcomes; the cultural field has applied this type of data analysis in the context of movies. This paper is the first study to consider the impact of electronic word of mouth (eWOM) generated via social media in the context of performing arts. Compared to conventional word-of-mouth mechanisms, social media sites may further reduce the uncertainty caused by the ‘nobody knows’ principle by propagating an enormous amount of enduring and real-time information and opinions. This paper aims to test the potentiality of social media in understanding theater demand by combining booking data for the period 2010–2016 from the sales system of the Royal Danish Theater with volumetric data extracted from the theater’s official Facebook Page. In particular, we take into account the different possible relationships between the feedback provided by social media (in terms of ‘likes’ and comments) and the purchase of tickets by consumers: (1) eWOM influences tickets sale; (2) no causal relationship between eWOM and tickets sale as both reflect unobserved characteristics of the theater production; (3) tickets sale influences eWOM activities; (4) ticket sale influences eWOM which in turn influences ticket sale and so on. The results suggest that only the number of likes, rather than the Facebook comments, is related to the decision to purchase a ticket. In particular, there is a mutual interaction between the number of likes given to posts specifically dedicated to a given production and the number of tickets sold concerning that specific production: eWOM activity (in terms of “like”) influences the tickets sale, which in turn generates eWOM activity. With this study, we aim to show how social media data can constitute a new and effective tool for understanding theater demand.

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Notes

  1. Generally, the season starts in September and ends the following June.

  2. The 3SLS model is estimated assuming homoskedasticity, as the option for robust standard error is not available for the reg3 command in STATA.

  3. Also Duan et al. (2008) found a negative and significant coefficient of the second lag term of box-office revenue on the amount of eWOM. Their explanation is that there is a substitution effect between WOM volume between t and t − 2.

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Authors and Affiliations

  1. Department of Economics, Ca’ Foscari University of Venice, Cannaregio 873, 30121, Venice, Italy

    Andrea Baldin

  2. Department of Business Humanities and Law, Copenhagen Business School, Porcelænshaven 18B, 2000, Frederiksberg, Denmark

    Andrea Baldin & Trine Bille

  3. Department of Digitalization, Copenhagen Business School, Howitzvej 60, 2000, Frederiksberg, Denmark

    Raghava Rao Mukkamala

  4. Centre for Digital Enterprise Analytics and Leadership (DEAL), Ted Rogers School of Management, Toronto Metropolitan University, Toronto, Canada

    Ravi Vatrapu

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  1. Andrea Baldin
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  2. Trine Bille
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  3. Raghava Rao Mukkamala
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  4. Ravi Vatrapu
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Correspondence to Andrea Baldin.

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Baldin, A., Bille, T., Mukkamala, R.R. et al. The impact of social media activities on theater demand. J Cult Econ 48, 199–220 (2024). https://doi.org/10.1007/s10824-023-09480-z

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