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Artificial Intelligence and Entrepreneurship Research  

Martin Obschonka and Christian Fisch

Advances in Artificial Intelligence (AI) are intensively shaping businesses and the economy as a whole, and AI-related research is exploding in many domains of business and management research. In contrast, AI has received relatively little attention within the domain of entrepreneurship research, while many entrepreneurship scholars agree that AI will likely shape entrepreneurship research in deep, disruptive ways. When summarizing both the existing entrepreneurship literature on AI and potential avenues for future research, the growing relevance of AI for entrepreneurship research manifests itself along two dimensions. First, AI applications in the real world establish a distinct research topic (e.g., whether and how entrepreneurs and entrepreneurial ventures use and develop AI-based technologies, or how AI can function as an external enabler that generates and enhances entrepreneurial outcomes). In other words, AI is changing the research object in entrepreneurship research. The second dimension refers to drawing on AI-based research methods, such as big data techniques or AI-based forecasting methods. Such AI-based methods open several avenues for researchers to gain new, influential insights into entrepreneurs and entrepreneurial ventures that are more difficult to assess using traditional methods. In other words, AI is changing the research methods. Given that, so far, human intelligence could not fully uncover and comprehend the secrets behind the entrepreneurial process that is so deeply embedded in uncertainty and opportunity, AI-supported research methods might achieve new breakthrough discoveries. We conclude that the field needs to embrace AI as a topic and research method more enthusiastically while maintaining the essential research standards and scientific rigor that guarantee the field’s well-being, reputation, and impact.


Entrepreneurial Teams  

Nicola Breugst

Entrepreneurial teams develop and exploit ideas in order to turn them into entrepreneurial ventures that they jointly own and manage. While these teams are crucial drivers for the success of their ventures, their work can be challenging because they operate under conditions of high autonomy, uncertainty, and interdependence. Thus, it is important to understand how entrepreneurial teams work together and jointly advance their ventures. Research has followed three overarching approaches to explore how entrepreneurial teams can succeed in their endeavors. First, one stream of research has aimed at connecting team inputs, such as team members’ experiences, to firm-level outcomes. In a second stream of research, scholars have focused on what happens within entrepreneurial teams in terms of team processes and emergent states. This approach has identified various mechanisms that translate inputs into outcomes. Third, an increasing number of studies have started to unravel the complexities that entrepreneurial teams experience in their work. Specifically, this research has considered the mutual influence of team members and has explored how teams work on their tasks and are shaped by this work. Despite these advancements, entrepreneurial team research faces numerous challenges arising from the complex interplay of team members and their ventures as well as from access to high-quality data. Because of these and other challenges, many research questions around entrepreneurial teams still need to be addressed to better understand their work. These emerging research efforts are likely to be facilitated by additional data sources, such as educational programs devoted to advancing entrepreneurial teams and modern technologies promising better access to rich data. Overall, entrepreneurial team research not only contributes to a more nuanced understanding of the entrepreneurial process but also provides support for these teams as they create and nurture their ventures.