The recent rise of big data and artificial intelligence (AI) is changing markets, politics, organizations, and societies. It also affects the domain of research. Supported by new statistical methods that rely on computational power and computer science—data science methods—we are now able to analyze data sets that can be huge, multidimensional, and unstructured and are diversely sourced. In this paper, we describe the most prominent data science methods suitable for entrepreneurship research and provide links to literature and Internet resources for self-starters. We survey how data science methods have been applied in the entrepreneurship research literature. As a showcase of data science techniques, based on a dataset of 95% of all job vacancies in the Netherlands over a 6-year period with 7.7 million data points, we provide an original analysis of the demand dynamics for entrepreneurial skills in the Netherlands. We show which entrepreneurial skills are particularly important for which type of profession. Moreover, we find that demand for both entrepreneurial and digital skills has increased for managerial positions, but not for others. We also find that entrepreneurial skills were significantly more demanded than digital skills over the entire period 2012–2017 and that the absolute importance of entrepreneurial skills has even increased more than digital skills for managers, despite the impact of datafication on the labor market. We conclude that further studies of entrepreneurial skills in the general population—outside the domain of entrepreneurs—is a rewarding subject for future research.
|Journal||Small Business Economics: An International Journal|
|Publication status||Published - Oct 2020|
- data science
- machine learning
- entrepreneurial skills
- big data
- artificial intelligence