Inside the Engine Room of Digital Platforms: Reviews, Ratings, and Recommendations

Belleflamme, Paul; Peitz, Martin

doi:10.2139/ssrn.3128141

Cited by 25 publications

(22 citation statements)

References 45 publications

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“…reducing transaction costs). Examples include databased search services, which provide a ranking of results that fit the preferences of the individual user, as well as databased recommendation services that suggest a user or a buyer other products that she has a high probability of being interested in as well (Belleflamme & Peitz 2018;). These individualized services, based on personalized data of the user, induce additional shopping as well as additional usage of the services by the consumers/users, thus increasing turnover and revenues.…”

Section: Discussionmentioning

confidence: 99%

The Economics of the German Investigation of Facebook’s Data Collection

2020

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The importance of digital platforms and related data-driven business models is ever increasing and poses challenges for the workability of competition in the respective markets (tendencies towards dominant platforms, paying-with-data instead of traditional money, privacy concerns, etc.). Due to such challenges, investigations of such markets are of high interest. One of recent cases is the investigation of Facebook's data collection practices by German competition authorities. Our paper, in contrast to the wide stream of legal studies on this case, aims to analyze whether Facebook's practices regarding data collection could constitute an abuse of market power from an economic perspective, more specifically against the background of modern data economics. In doing so we summarize the state of the advanced theories, including influences from behavioral economics, addressing such markets, and discuss four potential theories of harm.

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Section: Discussionmentioning

confidence: 99%

The Economics of the German Investigation of Facebook’s Data Collection

2020

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“…The utility of a user depends on the size and identity of other users through network-effects (Belleflamme and Peitz, 2018a). The network effects can increase the utility of users when the total number of users using the platform is higher (Rochet and Tiróle, 2003;Belleflamme and Peitz 2018b), thereby allowing the platform to charge more and extract more surplus. We argue that, especially in the case of online platforms, the value a user derives from joining the platform also depends on the type -good-ness or bad-ness -of other users, and not just the total number of users.…”

Section: Introductionmentioning

confidence: 99%

Screening mechanism when online users have privacy concerns

Jacob

2019

IJRM

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In consumer-to-consumer online platforms that enable selling (e.g., eBay, Taobao) or sharing (e.g., Airbnb, Uber) of goods and services, information asymmetry between providers (e.g., sellers, hosts, drivers) and consumers (e.g., buyers, guests, passengers) pose challenges. Such platforms facilitate transactions between users (providers and consumers), who are often strangers. Stricter screening, background cheeks, and identity verification requirements may reduce the probability of bad users entering the platform. However, users are reluctant to share personal information on the internet. We design a matching mechanism to maximise platform profit when users are heterogeneous with some more likely to be good than others, but the platform does not know who. We argue that in some cases, the platform increases its profit by allowing users with a higher probability of being bad to join as well.

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“…Users may like sex&crime stories, but they benefit from fellow citizens to be informed about other issues. 122 Such rating system are the source of positive network effects, as discussed inBelleflamme and Peitz (2018a). However, as they also discuss, private incentives of the intermediary and social benefits are often not fully aligned leading to biased or less informative rating systems.123 In Section 5, we shortly return to the protection of attention seekers.…”

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confidence: 99%

“…For an overview of how intermediaries manage network effects on their platform, seeBelleflamme and Peitz (2018b). Relatedly, if sellers compete with each other, intermediaries may also manage the degree of competition on their platform, seeBelleflamme and Peitz (2019).21 For an overview on the functioning and design of recommender and rating systems, seeEdelman (2017) andBelleflamme and Peitz (2018a).22 For a detailed exposition in the context of OTT, seePeitz and Valletti (2015).23 An economists' introduction to the net neutrality debate is provided by Greenstein,Peitz, and Valletti (2016).…”

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confidence: 99%

“…Quoted from Jonah Weiner, "The great race to ruling streaming TV", New York Times Magazine, July 20, 2019.39 We return to biased recommender systems further below. See alsoBelleflamme and Peitz (2018a).40 The matching occurs through a price mechanism, as advertisers bid for certain keywords conditional on certain user characteristics. For a formal analysis of the auction mechanism, seeEdelman, Ostrovsky, and Schwarz (2007).41 For an opposing view, seeFilistrucchi (2018).…”

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confidence: 99%

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Economic Policy for Digital Attention Intermediaries

Peitz

2020

SSRN Journal

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This report provides an overview on the economics of attention intermediaries. It addresses the following questions: What are the economics of attention intermediaries? For competition policy, how should markets be defined and market power of attention intermediaries be assessed? What theories of harm in merger control and abused of dominance possibly apply to attention intermediaries? The report also touches on consumer protection policies and other regulatory issues.

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Inside the Engine Room of Digital Platforms: Reviews, Ratings, and Recommendations

Cited by 25 publications

References 45 publications

The Economics of the German Investigation of Facebook’s Data Collection

The Economics of the German Investigation of Facebook’s Data Collection

Screening mechanism when online users have privacy concerns

Economic Policy for Digital Attention Intermediaries

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