Addictive Platforms

Ichihashi, Shota; Kim, Byung-Cheol

doi:10.2139/ssrn.3853961

Cited by 2 publications

(2 citation statements)

References 22 publications

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“…When platforms can endogenously choose the level of addictiveness, they opt to sacrifice quality for attention when consumers have a tight constraint on their attention level: in this situation, addictiveness does not change total attention. However, it influences how consumers split their attention between platforms: as such, increased competition could incentivise platforms to raise addictiveness and steal consumers from their rivals (Ichihashi & Kim, 2021).…”

Section: Attention Platformsmentioning

confidence: 99%

The microeconomics of data – a survey

Pino

2022

J. Ind. Bus. Econ.

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In recent years, academia, institutions, and policymakers have been focusing their attention on the impact of data in digital markets. The economic literature that explicitly models data and their collection as strategic variables is growing, but most studies focus on distinct settings with specific data uses. This survey aims to organise this literature to extract general insights that hold across different models and assumptions. To do so, I identify three classes of models according to the way they model data collection. I find that each class is characterised by a specific impact of data on the market outcomes, regardless of the specific data use. First, when firms obtain data without strategic interactions, their use has a pro-competitive effect on the market. However, firms fail to fully internalise the data externalities, leading to data overuse and, in turn, privacy concerns. Second, when firms collect data from their interaction with consumers, data can facilitate market tipping, especially if firms are asymmetric in their starting positions. Third, when firms acquire data from data intermediaries, data are strategically sold to temper competition in the downstream market, allowing intermediaries to extract most of the surplus at the expense of firms and consumers. These general insights can facilitate future research and help policymakers to have a more general understanding of the competitive effects of data, depending on the situation at hand.

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Section: Attention Platformsmentioning

confidence: 99%

The microeconomics of data – a survey

Pino

2022

J. Ind. Bus. Econ.

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“…relies on a custom-made application, whereas the primary data collection done in my paper relies on a (relatively) cheap, publicly available, parental control application and an open source Chrome extension which is more accessible to other researchers. Furthermore, unlike Allcott, Gentzkow and Song (2021), I can comprehensively track substitution towards other devices without having to rely on self-reported data.5 In the theory literature,Ichihashi and Kim (2021) study competition between addictive platforms where platforms trade off application quality for increased addictiveness. Hoong (2021) also studies the role of self-control issues in driving usage and commitment devices to reduce usage through a randomized experiment.…”

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confidence: 99%

Drivers of Digital Attention: Evidence from a Social Media Experiment

Aridor

2023

Proceedings of the 24th ACM Conference on Economics and Computation

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I study demand for social media services by conducting an experiment where I monitor how participants spend time on digital services and shut off access to Instagram or YouTube on their mobile phones. I characterize how participants substitute their time during and after the restrictions, which motivates estimating a model of time usage with inertia. I apply the substitution patterns observed during the restriction period and implied by the model with and without inertia to a relevant market definition exercise to conclude that relevant markets may be larger than those considered by regulatory authorities for social media applications.

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Addictive Platforms

Cited by 2 publications

References 22 publications

The microeconomics of data – a survey

The microeconomics of data – a survey

Drivers of Digital Attention: Evidence from a Social Media Experiment

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