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Ciara Heavin and Frederic Adam

Since the 1960s, information technology (IT)/information systems (IS) professionals, data practitioners, and senior managers have focused on developing decision support capabilities to enhance organizational decision making. Initially, this quest was mostly driven by successive generations of technological advances. However, in the last decade, the pace at which large volumes of diverse data can be collected and processed, new algorithmic advances, and the development of computational infrastructure such as graphics processing units (GPUs) and tensor processing units (TPUs) have created new opportunities for global businesses in areas such as financial services, manufacturing, retail, sports, and healthcare. At this point, it seems that most industries and public services could potentially be revolutionized by these new techniques. The word analytics has replaced the previous individual components of computerized decision support technologies that have been developed under various labels in the past (). Much of the traditional researcher and practitioner communities who were concerned with decision support, decision support systems (DSSs), and business intelligence (BI) have reoriented their attention to innovative tools and technologies to derive value from new data streams through artificial intelligence (AI) and analytics. Identifying the main areas of focus for decision support and analytics provides a stimulus for new ideas for researchers, managers, and IS/IT and data professionals. These stakeholders need to undertake new empirical studies that explain how analytics can be used to develop and enhance new forms of decision support while considering the dilemmas that may arise due to the data capture and analyses of new digital data streams.


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.