Big Data in the Lab – How Do Consumers Fare when Predictive Algorithms Work Against Them?
Abstract: This paper investigates how consumers fare when predictive algorithms are not (only) working for, but (also) against them: when they predict not only which product a consumer is interested in, but also how much he or she is willing to pay for it. Do consumers anticipate that this can happen and do they take into account that such an algorithm might even outsmart them? Will people make use of costly privacy protective alternatives in such markets and can their behavior be explained by the limited strategic sophistication model of level-k thinking?
