Por favor, use este identificador para citar o enlazar este ítem:
https://hdl.handle.net/11000/40191Registro completo de metadatos
| Campo DC | Valor | Lengua/Idioma |
|---|---|---|
| dc.contributor.author | Gonçalves Dosantos, Juan Carlos | - |
| dc.contributor.author | Martínez, Ricardo | - |
| dc.contributor.author | Sánchez Soriano, Joaquín | - |
| dc.contributor.other | Departamentos de la UMH::Estadística, Matemáticas e Informática | es_ES |
| dc.date.accessioned | 2026-07-14T08:21:05Z | - |
| dc.date.available | 2026-07-14T08:21:05Z | - |
| dc.date.created | 2025 | - |
| dc.identifier.citation | Journal of Economic Behavior & Organization | es_ES |
| dc.identifier.issn | 0167-2681 | - |
| dc.identifier.issn | 2328-7616 | - |
| dc.identifier.uri | https://hdl.handle.net/11000/40191 | - |
| dc.description.abstract | Digital streaming platforms , including Twitch, Spotify, Netflix, Disney+, and Kindle, have emerged as major sources of entertainment with significant growth potential. Many of these platforms distribute royalties among streamers, artists, producers, or writers based on their impact. In this paper, we measure the relevance of each of these contributors to the overall success of the platform, which can play a key role in revenue allocation. We perform an axiomatic analysis to provide normative foundations for four relevance metrics: the uniform, the subscriber-uniform, the proportional, and the subscriber-proportional indicators. The last two indicators implement the so-called pro-rata and user-centric models, which are extensively applied to distribute revenues in the music streaming market. The axioms we propose formalize different principles of fairness, stability, and non-manipulability, and are tailor-made for the streaming context. We complete our analysis with a case study that measures the influence of the 19 most-followed streamers worldwide on the Twitch platform. | es_ES |
| dc.format | application/pdf | es_ES |
| dc.format.extent | 16 | es_ES |
| dc.language.iso | eng | es_ES |
| dc.publisher | Elsevier | es_ES |
| dc.relation.ispartofseries | Vol. 232 | es_ES |
| dc.rights | info:eu-repo/semantics/openAccess | es_ES |
| dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
| dc.subject | measure | es_ES |
| dc.subject | relevance | es_ES |
| dc.subject | proportionality | es_ES |
| dc.subject | streaming | es_ES |
| dc.subject | axiom | es_ES |
| dc.subject.other | CDU::5 - Ciencias puras y naturales::51 - Matemáticas | es_ES |
| dc.subject.other | CDU::3 - Ciencias sociales::31 - Demografía. Sociología. Estadística::311 - Estadística | es_ES |
| dc.subject.other | CDU::3 - Ciencias sociales::33 - Economía | es_ES |
| dc.title | Measuring success in streaming platforms | es_ES |
| dc.type | info:eu-repo/semantics/article | es_ES |
| dc.relation.publisherversion | https://doi.org/10.1016/j.jebo.2025.106941 | es_ES |
JEBO_2025.pdf
957,8 kB
Adobe PDF
Compartir:
La licencia se describe como: Atribución-NonComercial-NoDerivada 4.0 Internacional.
.png)