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Article

From Decision Support to Analytics  

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.

Article

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.

Article

Innovation in Artificial Intelligence: Illustrations in Academia, Apparel, and the Arts  

Andreas Kaplan

Artificial intelligence (AI), commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation,” can be classified into analytical, human-inspired, and humanized AI depending upon its application of cognitive, emotional, and social intelligence. AI’s foundations took place in the 1950s. A sequence of vicissitudes of funding, interest in, and support for AI followed subsequently. In 2015 AlphaGo, Google’s AI-driven system, won against the human grandmaster in the highly complex board game Go. This is considered one of the most significant milestones in the development of AI and marks the starting of a new period, enabling several AI innovations in a variety of sectors and industries. Higher education, the fashion industry, and the arts serve as illustrations of areas wherein ample innovation based on AI occurs. Using these domains, various angles of innovation in AI can be presented and decrypted. AI innovation in higher education, for example, indicates that at some point, AI-powered robots might take over the role of human teachers. For the moment, however, AI in academia is solely used to support human beings, not to replace them. The apparel industry, specifically fast fashion—one of the planet’s biggest polluters—shows how innovation in AI can help the sector move toward sustainability and eco-responsibility through, among other ways, improved forecasting, increased customer satisfaction, and more efficient supply chain management. An analysis of AI-driven novelty in the arts, notably in museums, shows that developing highly innovative, AI-based solutions might be a necessity for the survival of a strongly declining cultural sector. These examples all show the role AI already plays in these sectors and its likely importance in their respective futures. While AI applications imply many improvements for academia, the apparel industry, and the arts, it should come as no surprise that it also has several drawbacks. Enforcing laws and regulations concerning AI is critical in order to avoid its adverse effects. Ethics and the ethical behavior of managers and leaders in various sectors and industries is likewise crucial. Education will play an additional significant role in helping AI positively influence economies and societies worldwide. Finally, international entente (i.e., the cooperation of the world’s biggest economies and nations) must take place to ensure AI’s benefit to humanity and civilization. Therefore, these challenges and areas (i.e., enforcement, ethics, education, and entente) can be summarized as the four summons of AI.