온라인 플랫폼 사업자가 보유하는 데이터 관련 시장지배력 판단
Determination of Market Dominance related to Data Owned by Online Platforms
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As the rapid development of big data technology makes it possible to extract deeper and more diverse values from data, the important of data is growing. Advances in the data-driven economy are closely linked to the emergence of online platforms. Due to the rise of big data business models based on online platforms, data is not solely used as a means of the decision-making process or a product, but considered as important input into services. As a result, a diverse and deeper access to data enables businesses to compete on innovation and development to accommodate user preferences. At the same time, the strengthening of data driven network effects supported by user feedback loop, coupled with economies of scale, raise concerns or an increasing likelihood for businesses to create market dominance based on their data or monopolize the market (“winner-takes-all”). Responding to such changing market dynamics requires great efforts to understand the underlying factors and market dynamics spurring the changes, and an in-depth analysis into such drivers and market dynamics should be a starting point to regulate them. To properly address concerns related to monopolization and foreclosure of data which might harm innovation and to prevent overly intrusive regulations from interfering with the development of the 4th Industrial Revolution and competition for innovation, it is necessary to closely examine the features of each online platform and data that may affect the anti-trust analysis and review the factors to be considered in determining market dominance based on such features. Traditional methods used to assess market dominance need to be complemented and revised to accommodate the two-sided nature and free marketability of online platforms in market definition and changes in the criteria for the assessment of market dominance resulting from factors other than traditional price such as the increase in quality, focus price, or information cost. Determining the formation of entry barriers for data requires specific and individual assessment on the network effects caused by multi-sided nature of relevant markets, the size of switching cost resulting from economies of scale, not only the degree of switching of users or multi-homing but also the nature and types of data, the structure in which collection, retention and utilization of data are made, the possibility of securing similar data by competitors or new entrants, and increase in the use value due to accumulation of large amount of data. Furthermore, a comprehensive consideration of the overall data value chain is necessary to determine the degree of market dominance since the economic value of data ecosystem is created through the data value chain and the effects that may occur in each part of the value chain should be taken into account comprehensively. Since the value that can be extracted from data varies greatly depending on the algorithm, etc., it is necessary to consider technical capabilities of analyzing and utilizing data. In addition, it is also necessary to take into account the role of the vibrant and innovative dynamic market in each phase of assessment ranging from the definition of relevant markets to the assessment of entry barriers.
Abstract
As the rapid development of big data technology makes it possible to extract deeper and more diverse values from data, the important of data is growing. Advances in the data-driven economy are closely linked to the emergence of online platforms. Due to the rise of big data business models based on online platforms, data is not solely used as a means of the decision-making process or a product, but considered as important input into services. As a result, a diverse and deeper access to data enables businesses to compete on innovation and development to accommodate user preferences. At the same time, the strengthening of data driven network effects supported by user feedback loop, coupled with economies of scale, raise concerns or an increasing likelihood for businesses to create market dominance based on their data or monopolize the market (“winner-takes-all”). Responding to such changing market dynamics requires great efforts to understand the underlying factors and market dynamics spurring the changes, and an in-depth analysis into such drivers and market dynamics should be a starting point to regulate them. To properly address concerns related to monopolization and foreclosure of data which might harm innovation and to prevent overly intrusive regulations from interfering with the development of the 4th Industrial Revolution and competition for innovation, it is necessary to closely examine the features of each online platform and data that may affect the anti-trust analysis and review the factors to be considered in determining market dominance based on such features. Traditional methods used to assess market dominance need to be complemented and revised to accommodate the two-sided nature and free marketability of online platforms in market definition and changes in the criteria for the assessment of market dominance resulting from factors other than traditional price such as the increase in quality, focus price, or information cost. Determining the formation of entry barriers for data requires specific and individual assessment on the network effects caused by multi-sided nature of relevant markets, the size of switching cost resulting from economies of scale, not only the degree of switching of users or multi-homing but also the nature and types of data, the structure in which collection, retention and utilization of data are made, the possibility of securing similar data by competitors or new entrants, and increase in the use value due to accumulation of large amount of data. Furthermore, a comprehensive consideration of the overall data value chain is necessary to determine the degree of market dominance since the economic value of data ecosystem is created through the data value chain and the effects that may occur in each part of the value chain should be taken into account comprehensively. Since the value that can be extracted from data varies greatly depending on the algorithm, etc., it is necessary to consider technical capabilities of analyzing and utilizing data. In addition, it is also necessary to take into account the role of the vibrant and innovative dynamic market in each phase of assessment ranging from the definition of relevant markets to the assessment of entry barriers.
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