Data (r)evolution – the economics of algorithmic search and recommender services

The paper analyses the economics behind algorithmic search and recommender services, based upon personalized user data. Such services play a paramount role for online services such as marketplaces (e.g. Amazon), audio streaming (e.g. Spotify), video streaming (e.g. Netflix, YouTube), app stores, social networks (e.g. Instagram, Tik Tok, Facebook, Twitter) and many more. We start with a systematic analysis of search and recommendation services as a commercial good, highlighting the changes to these services by the systematic use of algorithms. Then we discuss benefits and risk for welfare arising from the widespread employment of algorithmic search and recommendation systems. In doing so, we summarize the existing economics literature and go beyond its insights, including highlighting further research desires. Eventually, we derive regulatory and managerial implications drawing on the current state of academic knowledge.


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