Introducing a model of automated brand-generated content in an era of computational advertising
Journal of Advertising, 49(4).
Van Noort, G., Himelboim, I., Martin, J., & Collinger, T. (2020). Introducing a model of automated brand-generated content in an era of computational advertising.
Abstract: Advancements in computing, technology, and their applications to advertising enable marketers to deliver brand messages tailored to individuals and consumer segments. The growth of computational advertising (CA) has created new opportunities but also poses risks in the use of algorithms to generate and optimize the impact of such messages. This article addresses a particular domain influenced by these advancements, namely, automated brand-generated content. We offer an automated brand-generated content (ABC) model that posits two advances. First, rather than solely optimizing consumer data for enhanced impact of automated content, we submit, and provide extra key variables to further illustrate, that there is a desirable balance of both consumer and brand data as inputs to algorithms to serve short- and long-term impact goals. Second, this article guides research by addressing tensions between understanding the relationship between inputs and desired impacts (both short and long term) and proposing a research agenda for future work.
Disclosure-Driven Recognition of Native Advertising: A Test of Two Competing Mechanisms
Eline Brussee, Eva Van Reijmersdal, Nathaniel Evans, and Bart Wojdynski (forthcoming), “Disclosure-Driven Recognition of Native Advertising: A Test of Two Competing Mechanisms,” Journal of Interactive Advertising Abstract: This study aims […]