A perspective of the changes taking place

Authors

DOI:

https://doi.org/10.24137/raeic.3.6.1

Keywords:

television trends, public financing, advertising, pay tv, new model of consumption, television on demand, television over the top, big data, Netflix, regulation

Abstract

For understand how will be the television in the next future it is necessary to observe the main trends of recent times. The article looks at what happened in the fields of public financing, advertising and pay-tv subscription, and it suggests that future will not be very happy for them; in fact the analyzed trends show that their growth will be short or, in some case, there will be a decrease. However in the next future traditional television companies will need big resources to face growing competition.

Another issue is how is television audience changing. A growing part of the audience needs more freedom and flexibility in the choices, and  video on demand offers better condition of consumption to the viewers. Today Netflix is the more video on demand successfull company model,  and it is yet operating in hundreds of countries. This  model is analized to explain the main factors that make Netflix grow more and more. One of those factors is that Netflix uses big data algorithms to undestand the subscribers tastes, interests, needs, and then suggests the contents better corresponding to their desires.

The last part of the article looks at some contraddictions that european public Institutions must face when they want to regulate these new television models.

From one side the risk of companies like Netflix is that they will increase in Europe the consumption of television contents produced in Usa, in contraddiction with European Union policy, that should put limits to this expansion. But from the other side those companies will strongly push the traffic growth of new telecommunications networks, and consequentely the incomes growth. And the broadband netwok development is an European strategic objective, currently  slowed down by the high investment needed.

Downloads

Download data is not yet available.

References

Amatrian X. (2014),How does Netflix Movie Recommandation Algorithm Works, www.Quora.com

Barb, Broadcasters Audience Research Board (2016), Trendspotting, www.Barb.co.uk

Madrigal A. (2014), How Netflix Reverse Engineerd Hollywood, in The Atlantis 2/1/2014

Netflix (2016), Q4-15 Letter to Shareholders, www.Netflix.com

Oea-Observatoire Européen de l’Audiovisuel (2016), Yearbook Online Service, Strasbourg

Oea-Observatoire Européen de l’Audiovisuel (2015) The development of European market for on-demand audiovisual services, Council of Europe

Richeri G. (2012), Economia dei media, Laterza, Bari

Richeri G. (2014), Modelli economici in transizione, Problemi dell’informazione, n°1, 2014, Il Mulino, Bologna

Richeri G. (2016), Oltre il determinismo tecnologico, in M. Scaglioni, L. Barra (a cura di), "Storiografie della televisione", Unicopli, Milano.

Vanderbilt T. ( 2013), The Science behind the Netflix Algorithms that Decide what you’llwatch, in Wired, 8/7/2013

Villareal Y., James M. (2016), Netflix: The Most Feared Force in Hollywood, in Los Angeles Time 18/1/2016

Published

2016-11-14

How to Cite

A perspective of the changes taking place. (2016). Revista De La Asociación Española De Investigación De La Comunicación, 3(6), 2-10. https://doi.org/10.24137/raeic.3.6.1