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The Live Event Ecosystem: Local Media Against Global Ticket Sellers?

  • Raphael GieseckeEmail author
Chapter
Part of the Media Business and Innovation book series (MEDIA)

Abstract

When live events are discovered via friends’ recommendations and tickets bought from globally active agencies, what is the role of local media companies? Why do most publishing houses see revenues drop whereas some event related media businesses flourish in their niches? Based on in-depth interviews of sixteen event ecosystem organisations from four countries I performed an extensive thematic analysis of their existing and planned business models; examined power constellations, gatekeepers and the proximity between actors by drawing from network analysis; and synthesised a generic event ecosystem. My findings indicate that, in order to achieve convergence, the news media industry needs to learn from entertainment media industry; package the respective event; build and bridge networks; focus stronger; and ensure affinity between content and advertisements. Meanwhile global ticket sellers continue to expand their ties and consequently their revenues in the event ecosystem. Municipalities should concentrate on their core public functions and build on their institutional, cultural and local proximity while feeding event information into regional, national and city event calendars. Supporting convergence, media businesses typically connect various networks and thus should investigate and develop their potential network bridging power. This is especially valid for media technology providers whose software platforms are crucial for convergent operations of otherwise diverse media actors. In conclusion, I suggest that active exploring and exploiting of the ecosystem enables media business leaders to keep up with global actors.

Keywords

Business Model Event Information Media Industry Event Ecosystem Event Discovery 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

Cordial thanks to (in alphabetical order) Jan-Erik Andelin, Zeerim Cheung, Stina Giesecke, Janne Saarela, Camilla Stjernvall-Malmberg and Jesse Valli. This work was partially supported by the Finnish Funding Agency for Innovation TEKES as part of the Tivit Next Media programme.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.School of ScienceAalto UniversityEspooFinland

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