IDEAS home Printed from https://ideas.repec.org/a/spr/busres/v13y2020i3d10.1007_s40685-020-00133-x.html
   My bibliography  Save this article

On the current state of combining human and artificial intelligence for strategic organizational decision making

Author

Listed:
  • Anna Trunk

    (Friedrich-Alexander University Erlangen-Nuremberg)

  • Hendrik Birkel

    (Friedrich-Alexander University Erlangen-Nuremberg)

  • Evi Hartmann

    (Friedrich-Alexander University Erlangen-Nuremberg)

Abstract

Strategic organizational decision making in today’s complex world is a dynamic process characterized by uncertainty. Therefore, diverse groups of responsible employees deal with the large amount and variety of information, which must be acquired and interpreted correctly to deduce adequate alternatives. The technological potential of artificial intelligence (AI) is expected to offer further support, although research in this regard is still developing. However, as the technology is designed to have capabilities beyond those of traditional machines, the effects on the division of tasks and the definition of roles established in the current human–machine relationship are discussed with increasing awareness. Based on a systematic literature review, combined with content analysis, this article provides an overview of the possibilities that current research identifies for integrating AI into organizational decision making under uncertainty. The findings are summarized in a conceptual model that first explains how humans can use AI for decision making under uncertainty and then identifies the challenges, pre-conditions, and consequences that must be considered. While research on organizational structures, the choice of AI application, and the possibilities of knowledge management is extensive, a clear recommendation for ethical frameworks, despite being defined as a crucial foundation, is missing. In addition, AI, other than traditional machines, can amplify problems inherent in the decision-making process rather than help to reduce them. As a result, the human responsibility increases, while the capabilities needed to use the technology differ from other machines, thus making education necessary. These findings make the study valuable for both researchers and practitioners.

Suggested Citation

  • Anna Trunk & Hendrik Birkel & Evi Hartmann, 2020. "On the current state of combining human and artificial intelligence for strategic organizational decision making," Business Research, Springer;German Academic Association for Business Research, vol. 13(3), pages 875-919, November.
  • Handle: RePEc:spr:busres:v:13:y:2020:i:3:d:10.1007_s40685-020-00133-x
    DOI: 10.1007/s40685-020-00133-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40685-020-00133-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1007/s40685-020-00133-x?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
    ---><---

    References listed on IDEAS

    as
    1. Thomas Bolander, 2019. "What do we loose when machines take the decisions?," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 23(4), pages 849-867, December.
    2. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    3. Jarrahi, Mohammad Hossein, 2018. "Artificial intelligence and the future of work: Human-AI symbiosis in organizational decision making," Business Horizons, Elsevier, vol. 61(4), pages 577-586.
    4. Patrick L. Anderson, 2019. "Business strategy and firm location decisions: testing traditional and modern methods," Business Economics, Palgrave Macmillan;National Association for Business Economics, vol. 54(1), pages 35-60, January.
    5. Smith, Robert Elliott, 2016. "Idealizations of Uncertainty, and Lessons from Artificial Intelligence," Economics - The Open-Access, Open-Assessment E-Journal (2007-2020), Kiel Institute for the World Economy (IfW Kiel), vol. 10, pages 1-40.
    6. Denis Bouyssou & Marc Pirlot, 2008. "On some ordinal models for decision making under uncertainty," Annals of Operations Research, Springer, vol. 163(1), pages 19-48, October.
    7. Kirchkamp, Oliver & Strobel, Christina, 2019. "Sharing responsibility with a machine," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 80(C), pages 25-33.
    8. Christian Julmi, 2019. "When rational decision-making becomes irrational: a critical assessment and re-conceptualization of intuition effectiveness," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 291-314, April.
    9. Olga Metzger & Thomas Spengler, 2019. "Modeling rational decisions in ambiguous situations: a multi-valued logic approach," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 271-290, April.
    10. Tamar Kugler & Edgar E. Kausel & Martin G. Kocher, 2012. "Are Groups more Rational than Individuals? A Review of Interactive Decision Making in Groups," CESifo Working Paper Series 3701, CESifo.
    11. Tobias Rebs & Marcus Brandenburg & Stefan Seuring & Margarita Stohler, 2018. "Stakeholder influences and risks in sustainable supply chain management: a comparison of qualitative and quantitative studies," Business Research, Springer;German Academic Association for Business Research, vol. 11(2), pages 197-237, September.
    12. Enrica Carbone & Konstantinos Georgalos & Gerardo Infante, 2019. "Individual vs. group decision-making: an experiment on dynamic choice under risk and ambiguity," Theory and Decision, Springer, vol. 87(1), pages 87-122, July.
    13. Jordi Pereira & Mariona Vilà, 2016. "A new model for supply chain network design with integrated assembly line balancing decisions," International Journal of Production Research, Taylor & Francis Journals, vol. 54(9), pages 2653-2669, May.
    14. Glock, C. H. & Hochrein, S., 2011. "Purchasing Organization and Design: A Literature Review," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 57809, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    15. Ernan Haruvy & Elena Katok & Zhongwen Ma & Suresh Sethi, 2019. "Relationship-specific investment and hold-up problems in supply chains: theory and experiments," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 45-74, April.
    16. Sabrina Schneider & Michael Leyer, 2019. "Me or information technology? Adoption of artificial intelligence in the delegation of personal strategic decisions," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 40(3), pages 223-231, April.
    17. Schwenk, Charles & Valacich, Joseph S., 1994. "Effects of Devil's Advocacy and Dialectical Inquiry on Individuals versus Groups," Organizational Behavior and Human Decision Processes, Elsevier, vol. 59(2), pages 210-222, August.
    18. Agrawal, Ajay & Gans, Joshua S. & Goldfarb, Avi, 2019. "Exploring the impact of artificial Intelligence: Prediction versus judgment," Information Economics and Policy, Elsevier, vol. 47(C), pages 1-6.
    19. Stefano Fiori, 2011. "Forms of Bounded Rationality: The Reception and Redefinition of Herbert A. Simon's Perspective," Review of Political Economy, Taylor & Francis Journals, vol. 23(4), pages 587-612, October.
    20. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    21. Wright, Scott A. & Schultz, Ainslie E., 2018. "The rising tide of artificial intelligence and business automation: Developing an ethical framework," Business Horizons, Elsevier, vol. 61(6), pages 823-832.
    22. Herbert A. Simon, 1955. "A Behavioral Model of Rational Choice," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 69(1), pages 99-118.
    23. Navin K. Dev & Ravi Shankar & Angappa Gunasekaran & Lakshman S. Thakur, 2016. "A hybrid adaptive decision system for supply chain reconfiguration," International Journal of Production Research, Taylor & Francis Journals, vol. 54(23), pages 7100-7114, December.
    24. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338, August.
    25. Boone, Tonya & Ganeshan, Ram & Jain, Aditya & Sanders, Nada R., 2019. "Forecasting sales in the supply chain: Consumer analytics in the big data era," International Journal of Forecasting, Elsevier, vol. 35(1), pages 170-180.
    26. Daniel Kahneman, 2003. "Maps of Bounded Rationality: Psychology for Behavioral Economics," American Economic Review, American Economic Association, vol. 93(5), pages 1449-1475, December.
    27. Thomas Kourouxous & Thomas Bauer, 2019. "Violations of dominance in decision-making," Business Research, Springer;German Academic Association for Business Research, vol. 12(1), pages 209-239, April.
    28. TonyaBoone & Ram Ganeshan & Nada Sanders, 2018. "How Big Data Could Challenge Planning Processes across the Supply Chain," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 50, pages 19-24, Summer.
    29. Christian Mühlroth & Michael Grottke, 2018. "A systematic literature review of mining weak signals and trends for corporate foresight," Journal of Business Economics, Springer, vol. 88(5), pages 643-687, July.
    30. Lismont, Jasmien & Vanthienen, Jan & Baesens, Bart & Lemahieu, Wilfried, 2017. "Defining analytics maturity indicators: A survey approach," International Journal of Information Management, Elsevier, vol. 37(3), pages 114-124.
    31. Madalena Moreira & Benny Tjahjono, 2016. "Applying performance measures to support decision-making in supply chain operations: a case of beverage industry," International Journal of Production Research, Taylor & Francis Journals, vol. 54(8), pages 2345-2365, April.
    32. Gary Charness & Matthias Sutter, 2012. "Groups Make Better Self-Interested Decisions," Journal of Economic Perspectives, American Economic Association, vol. 26(3), pages 157-176, Summer.
    33. Danny Samson & Pat Foley & Heng Soon Gan & Marianne Gloet, 2018. "Multi-stakeholder decision theory," Annals of Operations Research, Springer, vol. 268(1), pages 357-386, September.
    34. Quick, Reiner & Welter, S. & Mayer, J. H., 2013. "Improving Environmental Scanning Systems Using Bayesian Networks," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 63543, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    35. Maryam Kouchaki & Isaac H. Smith & Ekaterina Netchaeva, 2015. "Not All Fairness Is Created Equal: Fairness Perceptions of Group vs. Individual Decision Makers," Organization Science, INFORMS, vol. 26(5), pages 1301-1315, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Emilia Vann Yaroson & Soumyadeb Chowdhury & Sachin Kumar Mangla & Prasanta Kumar Dey, 2024. "Unearthing the interplay between organisational resources, knowledge and industry 4.0 analytical decision support tools to achieve sustainability and supply chain wellbeing," Annals of Operations Research, Springer, vol. 342(2), pages 1321-1368, November.
    2. Alim Al Ayub Ahmed & Sugandha Agarwal & IMade Gede Ariestova Kurniawan & Samuel P. D. Anantadjaya & Chitra Krishnan, 2022. "Business boosting through sentiment analysis using Artificial Intelligence approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 699-709, March.
    3. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    4. Fotis Kitsios & Maria Kamariotou, 2021. "Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda," Sustainability, MDPI, vol. 13(4), pages 1-14, February.
    5. Dr. Betty Muthoni Njagi, 2022. "Community Service Learning as a New Discourse of Communion of Purpose for the Wellbeing of the Human Person," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 6(11), pages 205-210, November.
    6. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2021. "An Artificial Intelligence (Ai)-Readiness and Adoption Framework for Agritech Firms," QBS Working Paper Series 271255, Queen's University Belfast, Queen's Business School.
    7. Ivanov, Stanislav & Webster, Craig, 2024. "Automated decision-making: Hoteliers’ perceptions," Technology in Society, Elsevier, vol. 76(C).
    8. Issa, Helmi & Jabbouri, Rachid & Palmer, Mark, 2022. "An artificial intelligence (AI)-readiness and adoption framework for AgriTech firms," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Rengarajan, Srinath & Narayanamurthy, Gopalakrishnan & Moser, Roger & Pereira, Vijay, 2022. "Data strategies for global value chains: Hybridization of small and big data in the aftermath of COVID-19," Journal of Business Research, Elsevier, vol. 144(C), pages 776-787.
    10. Mahmoud Abdulhadi Alabdali & Sami A. Khan & Muhammad Zafar Yaqub & Mohammed Awad Alshahrani, 2024. "Harnessing the Power of Algorithmic Human Resource Management and Human Resource Strategic Decision-Making for Achieving Organizational Success: An Empirical Analysis," Sustainability, MDPI, vol. 16(11), pages 1-30, June.

    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. Christoph Keding, 2021. "Understanding the interplay of artificial intelligence and strategic management: four decades of research in review," Management Review Quarterly, Springer, vol. 71(1), pages 91-134, February.
    2. Keding, Christoph & Meissner, Philip, 2021. "Managerial overreliance on AI-augmented decision-making processes: How the use of AI-based advisory systems shapes choice behavior in R&D investment decisions," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    3. Carmen Isensee & Kai-Michael Griese & Frank Teuteberg, 2021. "Sustainable artificial intelligence: A corporate culture perspective [Sustainable artificial intelligence: Eine unternehmenskulturelle Perspektive]," Sustainability Nexus Forum, Springer, vol. 29(3), pages 217-230, December.
    4. Rim Lassoued & Hayley Hesseln & Peter W. B. Phillips & Stuart J. Smyth, 2020. "Effects of information presentation on regulatory decisions for products of biotechnology," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, vol. 8(3), pages 151-175, November.
    5. Timilsina, Raja R. & Kotani, Koji & Nakagawa, Yoshinori & Saijo, Tatsuyoshi, 2022. "Intragenerational deliberation and intergenerational sustainability dilemma," European Journal of Political Economy, Elsevier, vol. 73(C).
    6. Sendhil Mullainathan & Ziad Obermeyer, 2023. "Diagnosing Physician Error: A Machine Learning Approach to Low-Value Health Care," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 137(2), pages 679-727.
    7. Pietronudo, Maria Cristina & Croidieu, Grégoire & Schiavone, Francesco, 2022. "A solution looking for problems? A systematic literature review of the rationalizing influence of artificial intelligence on decision-making in innovation management," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    8. Reis, Carolina & Ruivo, Pedro & Oliveira, Tiago & Faroleiro, Paulo, 2020. "Assessing the drivers of machine learning business value," Journal of Business Research, Elsevier, vol. 117(C), pages 232-243.
    9. Vincent, Vinod U., 2021. "Integrating intuition and artificial intelligence in organizational decision-making," Business Horizons, Elsevier, vol. 64(4), pages 425-438.
    10. Joshua S. Gans, 2023. "Artificial intelligence adoption in a monopoly market," Managerial and Decision Economics, John Wiley & Sons, Ltd., vol. 44(2), pages 1098-1106, March.
    11. Shivam Gupta & Sachin Modgil & Samadrita Bhattacharyya & Indranil Bose, 2022. "Artificial intelligence for decision support systems in the field of operations research: review and future scope of research," Annals of Operations Research, Springer, vol. 308(1), pages 215-274, January.
    12. Legenzova Renata & Leckė Gintarė, 2022. "Exploring Lithuanian Real Estate Crowdfunding Investors’ Rationality," Management of Organizations: Systematic Research, Sciendo, vol. 87(1), pages 83-102, June.
    13. Jochen Wulf, 2020. "Development of an AHP hierarchy for managing omnichannel capabilities: a design science research approach," Business Research, Springer;German Academic Association for Business Research, vol. 13(1), pages 39-68, April.
    14. Faralla, Valeria & Borà, Guido & Innocenti, Alessandro & Novarese, Marco, 2020. "Promises in group decision making," Research in Economics, Elsevier, vol. 74(1), pages 1-11.
    15. Ramm, Joachim & Tjøtta, Sigve & Torsvik, Gaute, 2013. "Incentives and creativity in groups," Working Papers in Economics 06/13, University of Bergen, Department of Economics.
    16. Colombier, Carsten, 2011. "Konjunktur und Wachstum [Business cycles fluctuations and long-term growth]," MPRA Paper 104739, University Library of Munich, Germany.
    17. Michaël Lainé, 2014. "Vers une alternative au paradigme de la rationalité ? Victoires et déboires du programme spinoziste en économie," Post-Print hal-01335618, HAL.
    18. David Stadelmann & Benno Torgler, 2012. "Bounded Rationality and Voting Decisions Exploring a 160-Year Period," Working Papers 2012.70, Fondazione Eni Enrico Mattei.
    19. Wüstenhagen, Rolf & Menichetti, Emanuela, 2012. "Strategic choices for renewable energy investment: Conceptual framework and opportunities for further research," Energy Policy, Elsevier, vol. 40(C), pages 1-10.
    20. Carolin V. Zorell, 2020. "Nudges, Norms, or Just Contagion? A Theory on Influences on the Practice of (Non-)Sustainable Behavior," Sustainability, MDPI, vol. 12(24), pages 1-21, December.

    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:busres:v:13:y:2020:i:3:d:10.1007_s40685-020-00133-x. 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.