Methods in Marketing: Leveraging Artificial Intelligence for Academic Information Dissemination
(A2024-119835)

AUTHORS

Maximilian Witte, University of Hamburg; Keno Tetzlaff, University of Hamburg; Martin Reisenbichler, University of Hamburg; Mark Heitmann, University of Hamburg

ABSTRACT

In the dynamically evolving field of marketing research, it is imperative that scholars follow the substantive and methodological developments. However, in the context of thousands of newly published articles each year, increasing diversity in methods, and further acceleration through the advent of artificial intelligence (AI), keeping track becomes a daunting task for marketing researchers. Some classical vehicles for information dissemination exist (e.g., editorials, benchmarks), but are typically not designed to cover developments in the entire marketing area and rely on error-prone manual work. We propose an AI-based approach for comprehensive and structured analysis of the entire marketing literature from 2010 onward. We demonstrate the increasing complexity in marketing (i.e., increase in both articles and methods per article), identify recent developments in the field, and strive to provide more structured access to article information.