IDEAS home Printed from https://ideas.repec.org/a/spr/binfse/v65y2023i6d10.1007_s12599-023-00810-1.html
   My bibliography  Save this article

Disentangling Human-AI Hybrids

Author

Listed:
  • Lukas Fabri

    (University of Applied Sciences Augsburg
    Branch Business and Information Systems Engineering of the Fraunhofer FIT)

  • Björn Häckel

    (University of Applied Sciences Augsburg
    Branch Business and Information Systems Engineering of the Fraunhofer FIT)

  • Anna Maria Oberländer

    (University of Bayreuth
    Branch Business and Information Systems Engineering of the Fraunhofer FIT)

  • Marius Rieg

    (University of Applied Sciences Augsburg
    Branch Business and Information Systems Engineering of the Fraunhofer FIT)

  • Alexander Stohr

    (Branch Business and Information Systems Engineering of the Fraunhofer FIT
    Kempten University of Applied Sciences)

Abstract

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.

Suggested Citation

  • Lukas Fabri & Björn Häckel & Anna Maria Oberländer & Marius Rieg & Alexander Stohr, 2023. "Disentangling Human-AI Hybrids," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 65(6), pages 623-641, December.
    • Handle: RePEc:spr:binfse:v:65:y:2023:i:6:d:10.1007_s12599-023-00810-1
    DOI: 10.1007/s12599-023-00810-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12599-023-00810-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12599-023-00810-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pär J. Ågerfalk, 2020. "Artificial intelligence as digital agency," European Journal of Information Systems, Taylor & Francis Journals, vol. 29(1), pages 1-8, January.
    2. Dennis Kundisch & Jan Muntermann & Anna Maria Oberländer & Daniel Rau & Maximilian Röglinger & Thorsten Schoormann & Daniel Szopinski, 2022. "An Update for Taxonomy Designers," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 64(4), pages 421-439, August.
    3. Dwivedi, Yogesh K. & Hughes, Laurie & Ismagilova, Elvira & Aarts, Gert & Coombs, Crispin & Crick, Tom & Duan, Yanqing & Dwivedi, Rohita & Edwards, John & Eirug, Aled & Galanos, Vassilis & Ilavarasan, , 2021. "Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy," International Journal of Information Management, Elsevier, vol. 57(C).
    4. Davenport, Thomas H., 2018. "The AI Advantage: How to Put the Artificial Intelligence Revolution to Work," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262039176, December.
    5. repec:dar:wpaper:137446 is not listed on IDEAS
    6. Gary C. Moore & Izak Benbasat, 1991. "Development of an Instrument to Measure the Perceptions of Adopting an Information Technology Innovation," Information Systems Research, INFORMS, vol. 2(3), pages 192-222, September.
    7. Zhang, Dan & Pee, L.G. & Cui, Lili, 2021. "Artificial intelligence in E-commerce fulfillment: A case study of resource orchestration at Alibaba’s Smart Warehouse," International Journal of Information Management, Elsevier, vol. 57(C).
    8. Jan Jöhnk & Malte Weißert & Katrin Wyrtki, 2021. "Ready or Not, AI Comes— An Interview Study of Organizational AI Readiness Factors," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(1), pages 5-20, February.
    9. Sturm, Timo & Gerlach, Jin & Pumplun, Luisa & Mesbah, Neda & Peters, Felix & Tauchert, Christoph & Nan, Ning & Buxmann, Peter, 2021. "Coordinating Human and Machine Learning for Effective Organizational Learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 125653, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    10. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    11. Robert C Nickerson & Upkar Varshney & Jan Muntermann, 2013. "A method for taxonomy development and its application in information systems," European Journal of Information Systems, Taylor & Francis Journals, vol. 22(3), pages 336-359, May.
    12. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    13. Daniel Szopinski & Thorsten Schoormann & Thomas John & Ralf Knackstedt & Dennis Kundisch, 2020. "Software tools for business model innovation: current state and future challenges," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(3), pages 469-494, September.
    14. Sowa, Konrad & Przegalinska, Aleksandra & Ciechanowski, Leon, 2021. "Cobots in knowledge work," Journal of Business Research, Elsevier, vol. 125(C), pages 135-142.
    15. Shin, Won & Han, Jeongyun & Rhee, Wonjong, 2021. "AI-assistance for predictive maintenance of renewable energy systems," Energy, Elsevier, vol. 221(C).
    16. Alexander Maedche & Christine Legner & Alexander Benlian & Benedikt Berger & Henner Gimpel & Thomas Hess & Oliver Hinz & Stefan Morana & Matthias Söllner, 2019. "AI-Based Digital Assistants," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 61(4), pages 535-544, August.
    17. Demlehner, Quirin & Schoemer, Daniel & Laumer, Sven, 2021. "How can artificial intelligence enhance car manufacturing? A Delphi study-based identification and assessment of general use cases," International Journal of Information Management, Elsevier, vol. 58(C).
    18. Ekaterina Jussupow & Kai Spohrer & Armin Heinzl & Joshua Gawlitza, 2021. "Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence," Information Systems Research, INFORMS, vol. 32(3), pages 713-735, September.
    19. Carsten Østerlund & Mohammad Hossein Jarrahi & Matthew Willis & Karen Boyd & Christine T. Wolf, 2021. "Artificial intelligence and the world of work, a co‐constitutive relationship," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 72(1), pages 128-135, January.
    20. Antje Janssen & Jens Passlick & Davinia Rodríguez Cardona & Michael H. Breitner, 2020. "Virtual Assistance in Any Context," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 62(3), pages 211-225, June.
    21. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Weber & Martin Engert & Norman Schaffer & Jörg Weking & Helmut Krcmar, 2023. "Organizational Capabilities for AI Implementation—Coping with Inscrutability and Data Dependency in AI," Information Systems Frontiers, Springer, vol. 25(4), pages 1549-1569, August.
    2. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    3. Sullivan, Yulia & Fosso Wamba, Samuel, 2024. "Artificial intelligence and adaptive response to market changes: A strategy to enhance firm performance and innovation," Journal of Business Research, Elsevier, vol. 174(C).
    4. Volkmar, Gioia & Fischer, Peter M. & Reinecke, Sven, 2022. "Artificial Intelligence and Machine Learning: Exploring drivers, barriers, and future developments in marketing management," Journal of Business Research, Elsevier, vol. 149(C), pages 599-614.
    5. Janarthanan Balakrishnan & Yogesh K. Dwivedi & Laurie Hughes & Frederic Boy, 2024. "Enablers and Inhibitors of AI-Powered Voice Assistants: A Dual-Factor Approach by Integrating the Status Quo Bias and Technology Acceptance Model," Information Systems Frontiers, Springer, vol. 26(3), pages 921-942, June.
    6. Akter, Shahriar & Dwivedi, Yogesh K. & Sajib, Shahriar & Biswas, Kumar & Bandara, Ruwan J. & Michael, Katina, 2022. "Algorithmic bias in machine learning-based marketing models," Journal of Business Research, Elsevier, vol. 144(C), pages 201-216.
    7. Brendan James Keegan & Denis Dennehy & Peter Naudé, 2024. "Implementing Artificial Intelligence in Traditional B2B Marketing Practices: An Activity Theory Perspective," Information Systems Frontiers, Springer, vol. 26(3), pages 1025-1039, June.
    8. Martin Haupt & Jan Freidank & Alexander Haas, 2025. "Consumer responses to human-AI collaboration at organizational frontlines: strategies to escape algorithm aversion in content creation," Review of Managerial Science, Springer, vol. 19(2), pages 377-413, February.
    9. Thorsten Schoormann & Julia Schweihoff & Ilka Jussen & Frederik Möller, 2023. "Classification tools for business models: Status quo, comparison, and agenda," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-36, December.
    10. Maggie Wang, Yazhu & Matook, Sabine & Dennis, Alan R., 2024. "Unintended consequences of humanoid service robots: A case study of public service organizations," Journal of Business Research, Elsevier, vol. 174(C).
    11. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    12. Sara Moussawi & Marios Koufaris & Raquel Benbunan-Fich, 2021. "How perceptions of intelligence and anthropomorphism affect adoption of personal intelligent agents," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(2), pages 343-364, June.
    13. Pramanik, Paritosh & Jana, Rabin K. & Ghosh, Indranil, 2024. "AI readiness enablers in developed and developing economies: Findings from the XGBoost regression and explainable AI framework," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    14. Weiguang Wang & Guodong (Gordon) Gao & Ritu Agarwal, 2024. "Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience," Management Science, INFORMS, vol. 70(9), pages 5753-5775, September.
    15. Abdelhalim, Esraa & Anazodo, Kemi Salawu & Gali, Nazha & Robson, Karen, 2024. "A framework of diversity, equity, and inclusion safeguards for chatbots," Business Horizons, Elsevier, vol. 67(5), pages 487-498.
    16. Chenfeng Yan & Quan Chen & Xinyue Zhou & Xin Dai & Zhilin Yang, 2024. "When the Automated fire Backfires: The Adoption of Algorithm-based HR Decision-making Could Induce Consumer’s Unfavorable Ethicality Inferences of the Company," Journal of Business Ethics, Springer, vol. 190(4), pages 841-859, April.
    17. Marcel Fassnacht & Jannis Leimstoll & Carina Benz & Daniel Heinz & Gerhard Satzger, 2024. "Data sharing practices: The interplay of data, organizational structures, and network dynamics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-25, December.
    18. Deng, Shichang & Zhang, Jingjing & Lin, Zhengnan & Li, Xiangqian, 2024. "Service staff makes me nervous: Exploring the impact of insecure attachment on AI service preference," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    19. Sundberg, Leif & Holmström, Jonny, 2023. "Democratizing artificial intelligence: How no-code AI can leverage machine learning operations," Business Horizons, Elsevier, vol. 66(6), pages 777-788.
    20. Daniela Corsaro & Isabella Maggioni, 2021. "Managing the sales transformation process in B2B: between human and digital," Italian Journal of Marketing, Springer, vol. 2021(1), pages 25-56, June.

    Corrections

    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:65:y:2023:i:6:d:10.1007_s12599-023-00810-1. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.