Memorias de investigación
Ponencias en congresos:
Social Network Marketing: A Segmentation Approach to Understanding Purchase Intention
Año:2016

Áreas de investigación
  • Psicología del marketing y del comportamiento del consumidor

Datos
Descripción
This study investigates the effect of online and offline pre-purchase influences and the role of fashion brand involvement and online brand engagement in predicting purchase intention of products marketed in social media. A four-construct structural model was developed and validated on a sample of 799 shoppers in North America. Partial least squares structural equation modeling (PLS-SEM) was used to test the model. All six hypotheses were supported and fashion brand involvement was identified as a mediator. The analysis incorporated an advanced segmentation technique, Partial Least Squares Prediction Oriented Segmentation, (PLS-POS). Two groups of similar size emerged with differences that are of theoretical and managerial interest. Expansion of the model and future testing in different contexts will help to refine and develop it, providing insights into social media marketing.
Internacional
Si
Nombre congreso
Social Media and Society 2016 (SMSociety '16)
Tipo de participación
960
Lugar del congreso
Londres (Reino Unido)
Revisores
Si
ISBN o ISSN
978-1-4503-3938-4
DOI
10.1145/2930971.2930992
Fecha inicio congreso
11/07/2016
Fecha fin congreso
13/07/2016
Desde la página
1
Hasta la página
10
Título de las actas
Proceedings of the 7th 2016 International Conference on Social Media & Society

Esta actividad pertenece a memorias de investigación

Participantes
  • Autor: Donna Smith Ryerson University
  • Autor: Angel Hernandez Garcia UPM
  • Autor: Angel Francisco Agudo Peregrina UPM
  • Autor: Joseph F. Hair, Jr. Kennesaw State University

Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Innovación, Propiedad industrial y Política tecnológica (INNOPRO)
  • Centro o Instituto I+D+i: Centro de Domótica Integral, CEDINT
  • Departamento: Ingeniería de Organización, Administración de Empresas y Estadística