Jennifer Rix

Jennifer Rix is currently a doctoral candidate and research assistant at the Institute for Digital Management and New Media at Ludwig-Maximilians-University (LMU) Munich. Prior to her dissertation, Jennifer completed a B.Sc. from the University of Mannheim as well as a M.Sc. from LMU Munich and collected practical experience in strategy consulting and media organizations. Her research is rooted in the Information Systems (IS) discipline. In her dissertation, she focuses on how media organizations can effectively leverage generative AI in the form of algorithmic journalism. More specifically, she investigates how to integrate algorithmic journalists as novel teammates in the newsroom and inquiries into a potential revenue-side backlash of algorithmic authorship of journalistic articles. Jennifer’s goal is to develop a holistic understanding of how to leverage novel forms of generative AI in media organizations. This topic just recently received increased public and legislative scrutiny given the recent advances in large language models, making it a valuable and deep-pocketed field for research and practice.

In the underlying project, which was generously enabled by Sawtooth Software, she and her co-authors inquired into the effect of an algorithmic (instead of human) source of a news article on the valuation of this article. They operationalized the source as an attribute in a Choice-Based Conjoint (CBC) analysis. To capture the value, they also integrated the price of the article as an attribute by simulating a pay-per-article journalistic platform. Moreover, they have not only carried out the CBC via a crowdsourcing platform but also interviewed numerous consumers to identify further antecedents (i.e., further attributes) to the valuation of algorithm-created content. The research project is currently under review for publication.