TY - JOUR
T1 - Data science for entrepreneurship research
T2 - studying demand dynamics for entrepreneurial skills in the Netherlands
AU - Prüfer, Jens
AU - Prüfer, Patricia
N1 - Funding Information:
We are grateful to Freek van Gils, George Knox, and Marcia den Uijl for comments on an earlier draft and to Pradeep Kumar and Chayanin Wipusanawan for valuable research assistance. All errors are our own.
Publisher Copyright:
© 2019, The Author(s).
PY - 2020/10/1
Y1 - 2020/10/1
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Big data
KW - Data science
KW - Entrepreneurial skills
KW - Entrepreneurship
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=85067263621&partnerID=8YFLogxK
U2 - 10.1007/s11187-019-00208-y
DO - 10.1007/s11187-019-00208-y
M3 - Article
AN - SCOPUS:85067263621
VL - 55
SP - 651
EP - 672
JO - Small Business Economics
JF - Small Business Economics
SN - 0921-898X
IS - 3
ER -