Crossing the Line between Cool and Creepy – Non-Linearity of Personalization in Online Retailing
(A2020-63392)

AUTHORS

Alena Bermes, Heinrich-Heine-Universität Düsseldorf; Maximilian Hartmann, Heinrich-Heine-Universität Düsseldorf; Sebastian Danckwerts, Heinrich-Heine-Universität Düsseldorf

KEYWORDS

Personalized Product Recommendations; Non-Linearity; Privacy Calculus

ABSTRACT

Against the background of immense product choice in ecommerce, online retailers use personalized product recommendations to assist consumers in product search and selection. The personalization is based on individual preferences which consumers perceive to be both useful and privacy invasive. Prior research has focused on linear effects a consumer’s perceived personalization has on subsequent online purchase intention. By extending the privacy calculus, we aim to challenge this view. Our results indicate that perceived personalization initially has a positive influence on online purchase intention before this effect turns negative after a certain extent of personalization is reached. We thus identify the relationship as being non-linear and the risks as surpassing the benefits at some turning point. The findings have implications for researchers and practitioners because the degree of personalization should be described as an optimization instead of a maximization problem.