ABSTRACT
This chapter discusses the theory of performativity of economics is a particularly interesting approach to examine recommendation agents' agentivity. It explains the methodology that used to study the performativity of recommendation agents: in particular, the three intersecting methods used to study the design of – and the functions performed by – the recommendation agent developed by DataCrawler. The chapter shows how, in terms of its physical placement and cognitive architecture, DataCrawler's recommendation agent contains a usage scenario aimed at producing serendipitous effects. It also explains the way in which the DataCrawler agent effectively performs these serendipitous effects, leading consumers to discover, explore and select available products more easily from an online store, while simultaneously enrolling them in the buying process. The DataCrawler recommendation agent is a device that produces serendipitous effects in digitalized markets. The purpose of DataCrawler's recommendation agent is to tie consumers to one or more available goods of the e-commerce site visited.
