Leveraging Algorithms for Textual Content Creation in Media Organizations

The traditional revenue streams of the media industry increasingly fade due to consumers' dwindling willingness to pay for content. Media organizations must find measures to enhance their efficiency to sustain a competitive advantage. These challenges drive media organizations' search for novel technological opportunities. Algorithmic journalism, which refers to applying algorithms of different complexity to create journalistic content with little or no human intervention, recently caught attention due to technological breakthroughs regarding large language models. It presents one of the most promising areas for leveraging artificial intelligence in media organizations and should accordingly be put at the top of their strategic agenda. However, integrating algorithmic journalism into media organizations is far from trivial. While technical challenges with integrating this digital innovation are well known, the social challenges related to it remain largely unknown. To ensure a holistic perspective on these challenges, it is integral to consider the perspectives of journalists working with the innovation and individuals consuming its products alike. This dissertation embraces four research projects that apply distinct methods, ranging from explorative qualitative data collection to explanatory quantitative experiments. Overall, the results of this dissertation provide nuanced insights into how media organizations can effectively leverage algorithms for textual content creation, which span from their integration into organizational structures to the commercialization of their outputs. Besides, the insights of this dissertation also extend the theorizing on novel interactions between humans and AI-enabled systems as well as their outputs.