Hybrid Intelligence Business Information Systems Engineering Springer

Bonisiwe Shabane

-Nov 29, 2025, 8:57 PM

hybrid intelligence business information systems engineering springer

This is a preview of subscription content, log in via an institution to check access. Price excludes VAT (USA) Tax calculation will be finalised during checkout. For further work on this topic see Dellermann et al. (2019). https://deepmind.com (accessed 19 Mar 2019). https://ai.google/research/teams/brain/pair (accessed 19 Mar 2019).

Conceptualizing the Interworking of Humans and AI-Enabled Systems You have full access to this open access article Artificial intelligence (AI) offers great potential in organizations. The path to achieving this potential will involve human-AI interworking, as has been confirmed by numerous studies. However, it remains to be explored which direction this interworking of human agents and AI-enabled systems ought to take. To date, research still lacks a holistic understanding of the entangled interworking that characterizes human-AI hybrids, so-called because they form when human agents and AI-enabled systems closely collaborate.

To enhance such understanding, this paper presents a taxonomy of human-AI hybrids, developed by reviewing the current literature as well as a sample of 101 human-AI hybrids. Leveraging weak sociomateriality as justificatory knowledge, this study provides a deeper understanding of the entanglement between human agents and AI-enabled systems. Furthermore, a cluster analysis is performed to derive archetypes of human-AI hybrids, identifying ideal–typical occurrences of human-AI hybrids in practice. While the taxonomy creates a solid foundation for the understanding and analysis of human-AI hybrids, the archetypes illustrate the range of roles that AI-enabled systems can play in those interworking scenarios. Avoid common mistakes on your manuscript. Rapid advancements in the field of artificial intelligence (AI) have raised the level of expectations to the point at which some are even heralding AI as the next general-purpose technology (Goldfarb et al.

2019; Jöhnk et al. 2021). As AI-related technologies become ever more sophisticated, researchers and practitioners alike are identifying increasing numbers of AI use cases in the world of business (Bughin et al. 2018). At the same time, this growing potential of business applications has led to significant investments, which in turn has led to copious amounts of AI use cases (Dellermann et al. 2019a).

You have full access to this open access article The disruptive potential of artificial intelligence (AI) technologies involves creating new entrepreneurial opportunities and reshaping the entrepreneurial process. The impact of AI technologies on entrepreneurial activity is also reflected in an explosive level of research interest, leading to the fragmentation of existing studies. This phenomenon makes generating a comprehensive and systematic overview challenging. This paper reviews the existing research on the application of AI-based technologies in entrepreneurial practice. Specifically, it conducts a hybrid literature review, analyzing 345 articles from peer-reviewed journals.

It identifies the main contributions to the field; the conceptual, social, and intellectual structures; and the leading themes addressed to date. Despite growing interest in the field, this study concludes that most academic research on the subject has been superficial. This study proposes future lines of research based on the antecedents, decisions, and outcomes (ADO) and the theories, contexts, and methods (TCM) frameworks. Avoid common mistakes on your manuscript. According to the well-known U.S. dictionary Harper Collins, the 2023 word of the year was artificial intelligence (AI).

AI technologiesFootnote 1 such as machine learning (ML), the Internet of Things (IoT), automation, and natural language processing have made significant technological advances impacting virtually all industries and society. The emergence of the AI era has created the greatest entrepreneurial opportunity in the history of civilization (Iansiti and Lakhani 2020). Indeed, AI is an example of how radical external changes empower and enable new economic activities (Obschonka and Audretsch 2020a). Moreover, AI has crucial implications for how entrepreneurs develop, design, and scale their businesses during the entrepreneurial process (Chalmers et al. 2021). For example, AI can improve decision-making systems; improve process effectiveness, flexibility, and efficiency; increase productivity; reduce costs; or produce high-quality goods with high levels of customization (Giuggioli and Pellegrini 2023; Kraus et al.

2022a; Roppelt et al. 2023; Szukits and Móricz 2024; Zahlan et al. 2023). Furthermore, AI solutions are now easily accessible to entrepreneurs at a relatively affordable cost. The democratization of artificial intelligence enables entrepreneurs to compete even with large companies, thus levelling the technological playing field (Michael et al. 2023; Truong et al.

2023). This impact of AI on entrepreneurial activity has also attracted considerable interest from researchers in the field. However, existing studies are fragmented, making it challenging to generate a comprehensive and systematic overview. Hence, there is a strong need for a systematic literature review that considers evolution and the need for the establishment of theoretical frameworks to provide guidance and generalizability (when applicable) in research on entrepreneurs’... The importance of AI and entrepreneurship as a topic is reflected in three recent reviews. Despite these efforts, several gaps remain in the methodological and theoretical perspectives applied to understand entrepreneurs’ growing interest in adopting AI-based technologies.

Giuggioli and Pellegrini (2023) performed a qualitative analysis of AI's impact on entrepreneurship, presenting a framework that emphasizes AI's role in enhancing decision-making and fostering business opportunities. Li et al. (2022) conducted a bibliometric analysis of AI in entrepreneurial management, identifying key research clusters, but the analysis was limited by its scope and methodology. Blanco-González-Tejero et al. (2023) conducted a descriptive bibliometric study on AI and entrepreneurship, highlighting key topics but lacking in-depth analysis and future research proposals. This study aims to address these gaps by presenting a comprehensive and rigorous review, employing a hybrid systematic review approach that combines quantitative and qualitative methods to provide a holistic understanding of AI's impact...

Therefore, the study's central objectives are to synthesize existing knowledge, identify structural gaps, and establish theoretical frameworks to guide future research, thus fostering innovative research on AI technologies in entrepreneurial practice. (University of St. Gallen University of Kassel) (University of Kassel University of St. Gallen) As the access to this document is restricted, you may want to

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:binfse:v:61:y:2019:i:5:d:10.1007_s12599-019-00595-2. See general information about how to correct material in RePEc. If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item.

It also allows you to accept potential citations to this item that we are uncertain about. BISE (Business & Information Systems Engineering) is an international scholarly and double-blind peer reviewed journal that publishes scientific research on the effective and efficient design and utilization of information systems by individuals, groups, enterprises,... Information systems are understood as socio-technical systems comprising tasks, people, and information technology. Research published in the journal examines relevant problems in the analysis, design, implementation, and management of information systems. BISE has been the flagship journal of the German-language Information Systems community for more than 60 years. It is now one of the leading European journals in the field.

BISE is sponsored by the Section “Information Systems” (Wirtschaftsinformatik, WKWI) of the German Association for Business Research (VHB) and the special interest group “Business Informatics” (GI-FB WI) of the Gesellschaft für Informatik e. V. (GI) with more than 1200 members. BISE is also an affiliated journal of the Association for Information Systems (AIS). The contents of this e-journal are available in print and via Springerlink for universities with authorization. AIS members have access to the journal via the AIS eLibrary, others via DBLP.

Management summaries of selected research contributions are also published in WUM, a magazine for IT professionals published by Springer. BISE Homepage @ Springer Printed: bi-monthly (6 issues/year) Online: continuously

Hybrid Intelligence Business Information Systems Engineering Springer

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