Data science for entrepreneurship research:studying demand dynamics for entrepreneurial skills in the Netherlands

Prüfer, Jens ORCID: https://orcid.org/0000-0001-7203-9711 and Prüfer, Patricia (2020) Data science for entrepreneurship research:studying demand dynamics for entrepreneurial skills in the Netherlands. Small Business Economics, 55 (3). pp. 651-672. ISSN 0921-898X

Full text not available from this repository. (Request a copy)

Abstract

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.

Item Type: Article
Additional Information: 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).
Uncontrolled Keywords: artificial intelligence,big data,data science,entrepreneurial skills,entrepreneurship,machine learning,business, management and accounting(all),economics and econometrics ,/dk/atira/pure/subjectarea/asjc/1400
Faculty \ School: Faculty of Social Sciences > School of Economics
Related URLs:
Depositing User: LivePure Connector
Date Deposited: 08 Sep 2022 12:30
Last Modified: 23 Sep 2022 02:49
URI: https://ueaeprints.uea.ac.uk/id/eprint/87946
DOI: 10.1007/s11187-019-00208-y

Actions (login required)

View Item View Item