Title:	User-generated content in marketing research: methods and applications
Authors:	Dornekott, David
ORCID of the author:	https://orcid.org/0000-0002-4506-9154
Thesis advisor:	Prof. Dr. Bernhard Baumgartner
Thesis referee:	Prof. Dr. Valeriya Dinger
Abstract:	The totality of information submitted to digital platforms - such as social networks, microblogging services and review platforms - is summarily referred to as user-generated content and encompasses a broad variety of data types, ranging from text and images to audio and video recordings. A key characteristic of user-generated content is that it is both publicly available, as well as easily accessible and collectable in arbitrarily large amounts by anyone who has the necessary know-how and an interest in doing so. Within the context of marketing research, a subset of user-generated content representing consumers sharing their experience with and opinions about products, brands and services is a popular subject of investigation. The diversity and vastness of user-generated content presents several challenges and opportunities to marketing researchers. Accessing, managing and processing the large amounts of data involved requires methodological knowledge traditionally found in the realm of computer science and software engineering and the multitude of different platforms that act as data sources differ in their functioning, each requiring specific knowledge and experience in consequence. The four studies that make up this dissertation cover a wide range of topics and relate to different aspects of marketing in multiple ways and to varying degrees, building upon data collected from a multitude of sources over several years, including reddit, twitter and Twitch. All make use of the excessive amount of publicly available information that user-generated content represents and exemplify some of the ways that companies and marketing professionals can exploit it to better their understanding of the needs of their customers, develop more effective marketing measures and ultimately gain competitive advantage.
URL:	https://doi.org/10.48693/229 https://osnadocs.ub.uni-osnabrueck.de/handle/ds-202301278043
Subject Keywords:	marketing; user-generated content; natural language processing; social media; live streaming; retail investors
Issue Date:	27-Jan-2023
License name:	Attribution 3.0 Germany
License url:	http://creativecommons.org/licenses/by/3.0/de/
Type of publication:	Dissertation oder Habilitation [doctoralThesis]
Appears in Collections:	FB09 - E-Dissertationen

This item is licensed under a Creative Commons License