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BIG DATA AND THE ABUSE OF A DOMINANT POSITION BY DATA-DRIVEN ONLINE PLATFORMS UNDER EU COMPETITION LAW

BIG DATA AND THE ABUSE OF A DOMINANT POSITION BY DATA-DRIVEN ONLINE PLATFORMS UNDER EU COMPETITION LAW

IGA MAŁOBĘCKA-SZWAST

Wydawnictwo: C.H.BECK

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Opis

Opis produktu

ISBN: 978-83-8235-456-0

371 stron
format: A5
oprawa: twarda
Rok wydania: 2021

Data has become a crucial input of production for many services offered by online platforms and precondition of their competitive success.

Since providers of online platforms have become aware of the advantages derived from possessing and processing vast amounts of data, they increasingly adopt data-driven strategies to achieve and maintain their competitive data advantage over competitors.

The book:
- explores how exisiting competition tools and concepts used for assessing abuses of a dominant position can be applied to data-driven online platforms and competition concerns arising from their use of big data,
- attempts to suggest possible ways of its development, and
- analyses whether and if so, how the EU legal framework for the assessment of abuse of a dominant position under Article 102 TFEU should be adjusted to address it.

SPIS TREŚCI

List of Abbreviations

Introduction

Chapter 1. Big data as a fundament of˙data-driven online platforms‘
business˙models
1. Introduction
2. Concept and characteristics of big data
2.1. Definition of big data - the "4Vs of big data"
2.1.1. Volume
2.1.2. Velocity
2.1.3. Variety
2.1.4. Value
2.2. General economic characteristics of data
2.3. Distinction between personal and non-personal data and its
implications for the competition law
2.3.1. Definition of personal data and rules of its processing
2.3.2. Definition of non-personal data and rules applicable
to˙its˙processing
2.3.3. Implications of the distinction for the competition law
3. Big data as a source of competitive advantage for˙online platforms
3.1. Big data value creation cycle - from raw data to value
3.1.1. Data generation and data collection
3.1.2. Data storage and aggregation
3.1.3. Data processing and analysis
3.1.4. Data-driven decision-making (data usage) and data
monetisation
3.2. Big data as a source of efficiencies for online platforms
3.3. Data as an input of production or as a commodity
3.4. Big data as a barrier to entry and expansion
3.5. Determining the extent to which big data can˙be˙a˙competitive
advantage
4. Concluding remarks

Chapter 2. Characteristics of data-driven online platforms and its
implications for˙competition law analysis
1. Introduction
2. Definition and business models of data-driven online platforms
2.1. Definition of online platforms
2.2. Types of online platforms
2.3. Examples of data-driven online platforms and role of˙big data
in their businesses
2.3.1. Search engines
2.3.2. Social networks
2.3.3. E-commerce platforms
2.4. Revenue models of online platforms
3. Online platforms as multi-sided businesses
3.1. Concept of multi-sidedness
3.2. Multi-sidedness of online platforms
3.3. Implications of multi-sidedness for competition assessment
4. Other factors influencing functioning of data-driven online
platforms and their implications for˙competition policy
4.1. Economies of scale
4.2. Economies of scope
4.3. Network effects
4.4. Critical mass and chicken-and-egg problem
4.5. Positive feedback loop
4.5. Skewed pricing and phenomenon of "free" online˙services
4.6. Switching costs and consumer lock-in
4.7. Multi-homing and single-homing
4.8. Product differentiation
4.9. Innovation as a parameter of competition among online
platforms
4.10. Market tipping and competition for the market
("winner-takes-all")
5. Digital economy‘s characteristics as a barrier to˙entry
or expansion
6. Concluding remarks

Chapter 3: The EU framework for the assessment of market power
and its applicability to data-driven online platforms
1. Introduction
2. The challenge of defining the relevant market for˙data-driven
online platforms
2.1. Current framework for defining the relevant market
in the EU
2.2. Challenges resulting from multi-sided nature of˙online
platforms
2.3. Relevant product market for data-driven online platforms
2.3.1. Relevant product market for the user side
2.3.2. Relevant product market for the advertiser side
2.3.3. Relevant product market for data
2.3.4. Relevant geographic market for data-driven
online platforms
2.4. Applicability of the SSNIP test and alternative economic
tools for market definition to online platforms
2.4.1. Limited applicability of the SSNIP test to online
platforms
2.4.2. Small but Significant Non-transitory Decrease
in Quality (SSNDQ) test and other alternatives
2.5. Definition of the relevant market for data-driven online
platforms - interim conclusions
3. Current EU framework for the assessment of˙dominance
and its applicability to data-driven online platforms
3.1. The concept of market power and dominance
3.2. Implications of multi-sidedness for the assessment of˙market
power
3.3. Factors taken into account in the assessment of˙market
power and their applicability to online platforms
3.3.1. The relevance of market shares
3.3.2. Barriers to entry and expansion (potential competition)
3.3.3. Countervailing buyer power
3.4. Relevance of big data for the assessment of market power
of online platforms
3.4.1. Access to data
3.4.2. Big data technology
3.5. Towards a comprehensive framework for the assessment
of market power of data-driven online platforms
3.6. Concluding remarks

Chapter 4. Data-driven abuse of˙a˙dominant position
1. Introduction
2. Concept of abuse of a dominant position under˙Article 102 TFEU
3. Concept of data-driven abuse of a dominant position
4. Examples of data-driven exclusionary practices
4.1. General remarks on exclusionary practices
4.2. Refusal to give access to data - data as an essential facility
4.2.1. Essential facilities doctrine and refusal to supply
4.2.2. Applying essential facilities doctrine to data
4.2.3. Data as an essential facility
4.2.4. Data sharing obligations
4.3. Tying, bundling and leveraging data-advantage into
the other market
4.4. Exclusivity agreements that prevent competitors from
accessing data
4.5. Copying competitors‘ web content (scraping)
4.6. Potential data-driven abuses by vertically integrated
platforms
4.6.1. Preferential treatment of its own products (Google
Shopping case)
4.6.2. Use of competitors‘ data to favour its own products
(Amazon˙case)
5. Examples of data-driven exploitative practices
5.1. General remarks on exploitative practices
5.2. Excessive data collection
5.3. Personalised pricing and price discrimination enabled
by big data
5.4. Violation of data protection rules
5.5. Restrictions on data portability
6. Objective justification related to big data
7. Concluding remarks

Conclusions

Bibliography

List of Abbrevia

Kod wydawnictwa: 978-83-8235-456-0

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