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Top1. Introduction
Rapid and sustained changes in the economic, political, and social sectors also affect the research area. Vastly improved artificial intelligence (AI) is cheaper and more predictable (Agrawal et al., 2018). Improved forecasting allows you to work on a massive set of data that represents the entire. It contains merely near-complete data for that population. Furthermore, AI-dependent statistical methods (data science methods) can address new types of questions: On Twitter instead of traditional case studies? How can we categorize the characteristics of more than 1,000 chief executive officers (CEOs), identify entrepreneurs, and study how positive entrepreneurs are about firm performance? To what extent do entrepreneurs' skills and personality help workers in all fields and professions? Crucially, these questions (if questions can arise) have not been studied seriously in the last few decades by traditional empirical methods taught in economic and business graduate schools.
Shane and Venkataraman (2000) defined entrepreneurship as the identification, evaluation, and utilization of opportunities. Shane (2012) emphasized that entrepreneurship is a process, not a one-off event. The questions listed above relate to opportunities that have started with the latest advances in technology, and in itself, are ongoing processes. Thus, the purpose of entrepreneurship research changes with the development of the technological field. Today, because much more data and computer performance are available, these frontiers are largely shaped by the state of data science and technology. Today we can analyze and interpret large amounts of complex, unstructured data and predict based on correlation and inductive modeling. Researchers can benefit from understanding statistical methods driven by AI algorithms and accepting them where appropriate. This process has already started, and the economy (Einav & Levin 2014) and management (George et al. 2014). It has influenced social science, such as in 2014. It has created a new area of computational social science, which reveals new patterns of individual and collective behavior and allows models to be more accurate than economic and social interactions (Lazer et al., 2009).
We contribute in two ways to entrepreneurship literature. First, we investigate how data science methods were applied to entrepreneurial research literature and sketch how they were used to study important research issues that they could not study to the same extent. Second, we provide an original analysis of big data to predict venture capitalists' investment properties in Korea. Finally, we discuss the opportunities and risks of data science technology and relate them to traditional empirical research methods and theories.