Solving the Problem of Market Failure Associated with Data Scraping: The Behavior Regulatory Pattern

In the digital age, the business environment is experiencing unprecedented transformations caused by big data technologies that provide the resources for the survival and prosperity of business platforms as well as unique competitive advantages for long-term growth. As the data becomes more available, the importance of its effective protection against third-party scraping increases. However, the regulatory pattern of data protection remains a subject of debate, leaving the boundaries of data identification blurred. The study aims to design a methodological approach to analyzing the regulation of the market participants and their behavior under unfair competition to solve the problem of market failure associated with data scraping. Based on a survey conducted among managers of Chinese, American, German, and Russian business platforms, a behavior regulation model in data scraping has been developed. The key factors influencing the clarity of data identification boundaries were highlighted. An integral efficiency indicator was also introduced. The specific features of data scraping and methods of protection against it in the US, China, Germany, and Russia were revealed. Moreover, a correlation between the effectiveness of internal, and legal data protection and the net profit dynamics of business platforms in the studied countries has been found. The study has proved that the platform data collection boundaries can be accurately identified if all data collection factors are detailed.