IDEAS home Printed from https://ideas.repec.org/a/kap/jculte/v48y2024i2d10.1007_s10824-023-09480-z.html
   My bibliography  Save this article

The impact of social media activities on theater demand

Author

Listed:
  • Andrea Baldin

    (Ca’ Foscari University of Venice
    Copenhagen Business School)

  • Trine Bille

    (Copenhagen Business School)

  • Raghava Rao Mukkamala

    (Copenhagen Business School)

  • Ravi Vatrapu

    (Ted Rogers School of Management, Toronto Metropolitan University)

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.

Suggested Citation

  • Andrea Baldin & Trine Bille & Raghava Rao Mukkamala & Ravi Vatrapu, 2024. "The impact of social media activities on theater demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 48(2), pages 199-220, June.
  • Handle: RePEc:kap:jculte:v:48:y:2024:i:2:d:10.1007_s10824-023-09480-z
    DOI: 10.1007/s10824-023-09480-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10824-023-09480-z
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s10824-023-09480-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Marta Zieba, 2009. "Full-income and price elasticities of demand for German public theatre," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 33(2), pages 85-108, May.
    2. Stefan Tobias, 2004. "Quality in the Performing Arts: Aggregating and Rationalizing Expert Opinion," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 28(2), pages 109-124, May.
    3. Michael R. M. Abrigo & Inessa Love, 2016. "Estimation of panel vector autoregression in Stata," Stata Journal, StataCorp LP, vol. 16(3), pages 778-804, September.
    4. Jenkins, Stephen & Austen-Smith, David, 1987. "Interdependent decision-making in non-profit industries: A simultaneous equation analysis of English provincial theatre," International Journal of Industrial Organization, Elsevier, vol. 5(2), pages 149-174.
    5. Suman Basuroy & S. Abraham Ravid & Richard T. Gretz & B. J. Allen, 2020. "Is everybody an expert? An investigation into the impact of professional versus user reviews on movie revenues," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 44(1), pages 57-96, March.
    6. Jonathan Corning & Armando Levy, 2002. "Demand for Live Theater with Market Segmentation and Seasonality," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 26(3), pages 217-235, August.
    7. Andrea Baldin & Trine Bille & Andrea Ellero & Daniela Favaretto, 2018. "Revenue and attendance simultaneous optimization in performing arts organizations," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 42(4), pages 677-700, November.
    8. José Grisolía & Kenneth Willis, 2012. "A latent class model of theatre demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 113-139, May.
    9. K. Willis & J. Snowball, 2009. "Investigating how the attributes of live theatre productions influence consumption choices using conjoint analysis: the example of the National Arts Festival, South Africa," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 33(3), pages 167-183, August.
    10. Sacit Akdede & John King, 2006. "Demand for and productivity analysis of Turkish public theater," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 30(3), pages 219-231, December.
    11. Daniel Urrutiaguer, 2002. "Quality Judgements and Demand for French Public Theatre," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 26(3), pages 185-202, August.
    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. Wiśniewska Aleksandra, 2019. "Quality attributes in the non-market stated-preference based valuation of cultural goods," Central European Economic Journal, Sciendo, vol. 6(53), pages 132-150, January.
    2. Alina R. Buzanakova & Evgeniy M. Ozhegov, 2016. "Demand for Performing Arts: The Effect of Unobserved Quality on Price Elasticity," HSE Working papers WP BRP 156/EC/2016, National Research University Higher School of Economics.
    3. Jani-Petri Laamanen, 2013. "Estimating demand for opera using sales system data: the case of Finnish National Opera," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 37(4), pages 417-432, November.
    4. Junlong Wu & Keshen Jiang & Chaoqing Yuan, 2019. "Determinants of demand for traditional Chinese opera," Empirical Economics, Springer, vol. 57(6), pages 2129-2148, December.
    5. Ozhegova, Alina & Ozhegov, Evgeniy M., 2020. "Segmentation of theatre audiences: A latent class approach for combined data," Journal of choice modelling, Elsevier, vol. 37(C).
    6. José M. Grisolía & Kenneth G. Willis, 2016. "Consumer choice of theatrical productions: a combined revealed preference–stated preference approach," Empirical Economics, Springer, vol. 50(3), pages 933-957, May.
    7. José Grisolía & Kenneth Willis, 2012. "A latent class model of theatre demand," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 113-139, May.
    8. Ozhegova, A. & Ozhegov, E., 2018. "Estimation of Demand Function for Performing Arts: Empirical Analysis," Journal of the New Economic Association, New Economic Association, vol. 37(1), pages 87-110.
    9. Daniel Urrutiaguer, 2011. "Theatre," Chapters, in: Ruth Towse (ed.), A Handbook of Cultural Economics, Second Edition, chapter 59, Edward Elgar Publishing.
    10. Aleksandra Wiśniewska, 2019. "‘Quality food’ for cultural policies. Quality attributes in the non-market stated-preference based valuation of cultural goods," Working Papers 2019-03, Faculty of Economic Sciences, University of Warsaw.
    11. Alina Ozhegova & Evgeniy M. Ozhegov, 2018. "Heterogeneity in demand for performances and seats in the theatre," Journal of Revenue and Pricing Management, Palgrave Macmillan, vol. 17(3), pages 131-145, June.
    12. Evgeniy M. Ozhegov & Alina Ozhegova, 2018. "Segmentation of Theatre Audiences: A Latent Class Approach for Combined Data," HSE Working papers WP BRP 198/EC/2018, National Research University Higher School of Economics.
    13. Aleksandra Wiśniewska & Mikołaj Czajkowski, 2015. "Utilizing the Discrete Choice Experiment Approach for Designing a Socially Efficient Cultural Policy: The case of municipal theaters in Warsaw," Working Papers 2015-36, Faculty of Economic Sciences, University of Warsaw.
    14. David Throsby & John R. Severn & Katya Petetskaya, 2024. "Preference formation in demand for live theatre," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 48(2), pages 285-310, June.
    15. Kristien Werck & Bruno Heyndels, 2007. "Programmatic choices and the demand for theatre: the case of Flemish theatres," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 31(1), pages 25-41, March.
    16. Marta Zieba, 2017. "Cultural participation of tourists – Evidence from travel habits of Austrian residents," Tourism Economics, , vol. 23(2), pages 295-315, March.
    17. Andrea Baldin & Trine Bille, 2018. "Modelling preference heterogeneity for theatre tickets: a discrete choice modelling approach on Royal Danish Theatre booking data," Applied Economics, Taylor & Francis Journals, vol. 50(5), pages 545-558, January.
    18. Eric Kolhede & J. Tomas Gomez-Arias & Anna Maximova, 2023. "Price elasticity in the performing arts: a segmentation approach," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(3), pages 523-550, September.
    19. Marvao, Catarina & Borowiecki, Karol, 2015. "Dance Participation and Attendance in Denmark," SITE Working Paper Series 33, Stockholm School of Economics, Stockholm Institute of Transition Economics.
    20. K. Willis & J. Snowball & C. Wymer & José Grisolía, 2012. "A count data travel cost model of theatre demand using aggregate theatre booking data," Journal of Cultural Economics, Springer;The Association for Cultural Economics International, vol. 36(2), pages 91-112, May.

    More about this item

    Keywords

    Theater demand; Social media; Word of mouth;
    All these keywords.

    JEL classification:

    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Z10 - Other Special Topics - - Cultural Economics - - - General

    Statistics

    Access and download statistics

    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:kap:jculte:v:48:y:2024:i:2:d:10.1007_s10824-023-09480-z. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.