Analyzing Driving Factors of User-generated Content on YouTube and its Influence on Consumers Perceived Value

Resume

This work is cataloged in the Scientific Portal. Officially published in ICMarkTech (International Conference on Marketing and Technologies) 2022 (2022). Referenced as: "Torres, Ana; Pilar, Pedro; Santos, José Duarte; Pereira, Inês Veiga; Pires, Paulo Botelho".

The complete abstract and full text can be accessed directly at the official DOI: https://doi.org/10.1007/978-981-19-9099-1_47

Bibliographic & Editorial Details

Venue / Journal / Book ICMarkTech (International Conference on Marketing and Technologies) 2022

Publisher Springer

Pagination Pages 1667 - 1686

Publication Venue / Conference Santiago de Compostela, Spain

Role Coauthor

Official Identifiers

DOI: 10.1007/978-981-19-9099-1_47

Autores & Descoberta de Ciência ID

Identificação dos autores da instituição e deteção de coautores via FCT Ciência Vitae.

Autor(es) da Instituição

I

Inês Alexandra Barbosa da Veiga Pereira Beira Correia Investigador

Ciência ID: 6412-4E4D-5174

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J

José Duarte da Rocha Santos Investigador

Ciência ID: 811F-A8A7-0016

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P

Paulo Alexandre Botelho Rodrigues Pires Investigador

Ciência ID: B31E-755A-EE9E

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Coautores da Obra

Autores discriminados na citação da publicação:

Torres, Ana; Pilar, Pedro; Santos, José Duarte; Pereira, Inês Veiga; Pires, Paulo Botelho

Clique no botão acima para consultar a API da FCT e descobrir os perfis Ciência ID dos coautores.

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Analyzing Driving Factors of User-generated Content on YouTube and its Influence on Consumers Perceived Value 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Analyzing Driving Factors of User-generated Content on YouTube and its Influence on Consumers Perceived Value 🔓

Resume

Bibliographic & Editorial Details

Official Identifiers

Autores & Descoberta de Ciência ID

Nenhum coautor adicional com Ciência ID associado

Other Related Publications

AI-Driven Predictive Model for Diabetes Risk Assessment - A Class-Balanced Machine Learning Approach with Web-Based Deployment

SiSchool: A QR-Based Mobile Device Control System for Educational Environments

From predicting dropout rates to responsible intervention: explainable machine learning for academic success in higher education

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