While the disruptive potential of artificial intelligence (AI) and big data has been receiving growing attention and concern in a variety of research and application fields over the last few years, it has not received much scrutiny in contemporary entrepreneurship research so far. In its latest issue, Small Business Economics has just published a Special Issue on AI and entrepreneurship, with a collection of empirical AI studies addressing entrepreneurship. The Special Issue was edited by Martin Obschonka and David Audretsch.
In the editorial, the researchers present reflections on the role of AI and big data for this emerging area in the study and application of entrepreneurship research. While being mindful of the potentially overwhelming nature of the rapid progress in machine intelligence and other big data technologies for contemporary structures in entrepreneurship research, they put an emphasis on the reciprocity of the co-evolving fields of entrepreneurship research and practice. How can AI and big data contribute to a productive transformation of the research field and the real-world phenomena (e.g., “smart entrepreneurship”)? They also discuss ethical issues as well as challenges around a potential contradiction between entrepreneurial uncertainty and rule-driven AI rationality. The editorial gives researchers and practitioners orientation and showcases avenues and examples for concrete research in this field. At the same time, however, it is not unlikely that the entrepreneurship research field will encounter unforeseeable and currently inexplicable developments in the field soon. The editorial calls on entrepreneurship scholars, educators, and practitioners to proactively prepare for future scenarios.
Special Issue: Small Business Economics. Volume 55, issue 3, October 2020
Editorial: Obschonka, M., Audretsch, D.B. Artificial intelligence and big data in entrepreneurship: a new era has begun. Small Bus Econ 55, 529–539 (2020). https://doi.org/10.1007/s11187-019-00202-4
Paper 1: Coad, A., Srhoj, S. Catching Gazelles with a Lasso: Big data techniques for the prediction of high-growth firms. Small Bus Econ 55, 541–565 (2020). https://doi.org/10.1007/s11187-019-00203-3
Paper 2: Obschonka, M., Lee, N., Rodríguez-Pose, A. et al. Big data methods, social media, and the psychology of entrepreneurial regions: capturing cross-county personality traits and their impact on entrepreneurship in the USA. Small Bus Econ 55, 567–588 (2020). https://doi.org/10.1007/s11187-019-00204-2
Paper 3: Liebregts, W., Darnihamedani, P., Postma, E. et al. The promise of social signal processing for research on decision-making in entrepreneurial contexts. Small Bus Econ 55, 589–605 (2020). https://doi.org/10.1007/s11187-019-00205-1
Paper 4: Zhang, S.X., Van Burg, E. Advancing entrepreneurship as a design science: developing additional design principles for effectuation. Small Bus Econ 55, 607–626 (2020). https://doi.org/10.1007/s11187-019-00217-x
Paper 5: Kaminski, J.C., Hopp, C. Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals. Small Bus Econ 55, 627–649 (2020). https://doi.org/10.1007/s11187-019-00218-w
Paper 6: Prüfer, J., Prüfer, P. Data science for entrepreneurship research: studying demand dynamics for entrepreneurial skills in the Netherlands. Small Bus Econ 55, 651–672 (2020). https://doi.org/10.1007/s11187-019-00208-y
Paper 7: von Bloh, J., Broekel, T., Özgun, B. et al. New(s) data for entrepreneurship research? An innovative approach to use Big Data on media coverage. Small Bus Econ 55, 673–694 (2020). https://doi.org/10.1007/s11187-019-00209-x