Towards more effective consumer steering via network analysis

Jacopo Arpetti () and Antonio Iovanella ()
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Jacopo Arpetti: University of Rome Tor Vergata
Antonio Iovanella: University of Rome Tor Vergata

European Journal of Law and Economics, 2020, vol. 50, issue 3, No 3, 359-380

Abstract: Abstract Increased data gathering capacity, together with the spread of data analytics techniques, has prompterd an unprecedented concentration of information related to the individuals’ preferences in the hands of a few gatekeepers. In the present paper, we show how platforms’ performances still appear astonishing in relation to some unexplored data and networks properties, capable to enhance the platforms’ capacity to implement steering practices by means of an increased ability to estimate individuals’ preferences. To this end, we rely on network science whose analytical tools allow data representations capable of highlighting relationships between subjects and/or items, extracting a great amount of information. We therefore propose a measure called Network Information Patrimony, considering the amount of information available within the system and we look into how platforms could exploit data stemming from connected profiles within a network, with a view to obtaining competitive advantages. Our measure takes into account the quality of the connections among nodes as the one of a hypothetical user in relation to its neighbourhood, detecting how users with a good neighbourhood—hence of a superior connections set—obtain better information. We tested our measures on Amazons’ instances, obtaining evidence which confirm the relevance of information extracted from nodes’ neighbourhood in order to steer targeted users.

Keywords: Data value; Network-driven economy; Steering; Networks theory; Nearest neighbour degree (search for similar items in EconPapers)
JEL-codes: D83 D85 L11 (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (2)

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DOI: 10.1007/s10657-019-09637-2

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