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Cobots in knowledge work

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  • Sowa, Konrad
  • Przegalinska, Aleksandra
  • Ciechanowski, Leon

Abstract

Current technological developments, as well as widespread application of artificial intelligence, will doubtlessly continue to impact how people live and work. In this research, we explored synergies between human workers and AI in managerial tasks. We hypothesized that human-AI collaboration will increase productivity. In the paper, several levels of proximity between AI and humans in a work setting are distinguished. The multi-stage study, covering the exploratory phase in which we conducted a study of preferences using 10-item Likert scale, was conducted with a sample of 366 respondents. The study focused on working with different types of AI. The second and third phase of the study, in which we primarily used qualitative methods (scenario-based design combined with semi-structured interviews with six participants), focused on researching modes of collaboration between humans and virtual assistants. The study results generally confirmed our hypothesis about increased productivity due to enhanced human-AI collaboration, proving that the future of AI in knowledge work needs to focus not on full automation but rather on collaborative approaches where humans and AI work closely together.

Suggested Citation

  • Sowa, Konrad & Przegalinska, Aleksandra & Ciechanowski, Leon, 2021. "Cobots in knowledge work," Journal of Business Research, Elsevier, vol. 125(C), pages 135-142.
    • Handle: RePEc:eee:jbrese:v:125:y:2021:i:c:p:135-142
    DOI: 10.1016/j.jbusres.2020.11.038
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    as
    1. Mark Fenwick & Erik P. M. Vermeulen & Marcelo Corrales, 2018. "Business and Regulatory Responses to Artificial Intelligence: Dynamic Regulation, Innovation Ecosystems and the Strategic Management of Disruptive Technology," Perspectives in Law, Business and Innovation, in: Marcelo Corrales & Mark Fenwick & Nikolaus Forgó (ed.), Robotics, AI and the Future of Law, pages 81-103, Springer.
    2. B. S. Mordukhovich & T. T. A. Nghia & R. T. Rockafellar, 2015. "Full Stability in Finite-Dimensional Optimization," Mathematics of Operations Research, INFORMS, vol. 40(1), pages 226-252, February.
    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. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation, and Work," NBER Chapters, in: The Economics of Artificial Intelligence: An Agenda, pages 197-236, National Bureau of Economic Research, Inc.
    5. Pierpaolo Mastroianni & Bernardo Monechi & Carlo Liberto & Gaetano Valenti & Vito D P Servedio & Vittorio Loreto, 2015. "Local Optimization Strategies in Urban Vehicular Mobility," PLOS ONE, Public Library of Science, vol. 10(12), pages 1-13, December.
    6. Jack P. C. Kleijnen, 2015. "Simulation Optimization," International Series in Operations Research & Management Science, in: Design and Analysis of Simulation Experiments, edition 2, chapter 6, pages 241-300, Springer.
    7. Golov N.I. & Ronnback L., 2015. "SQL query optimization for highly normalized Big data," Бизнес-информатика, CyberLeninka;Федеральное государственное автономное образовательное учреждение высшего образования «Национальный исследовательский университет «Высшая школа экономики», issue 3 (33), pages 7-14.
    8. Lu Tong & Lei Nie & Zhenhuan He & Huiling Fu, 2015. "Optimization of Train Trip Package Operation Scheme," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-8, October.
    9. Xiaoxian He & Hanning Chen & Ben Niu & Jie Wang, 2015. "Root Growth Optimizer with Self-Similar Propagation," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-12, May.
    10. Bachmann, Rüdiger & Elstner, Steffen, 2015. "Firm optimism and pessimism," European Economic Review, Elsevier, vol. 79(C), pages 297-325.
    11. Li Li, 2015. "Selected Applications of Convex Optimization," Springer Optimization and Its Applications, Springer, edition 127, number 978-3-662-46356-7, June.
    12. Daron Acemoglu & Pascual Restrepo, 2018. "Artificial Intelligence, Automation and Work," Boston University - Department of Economics - Working Papers Series dp-298, Boston University - Department of Economics.
    13. Jin-ke Xiao & Wei-min Li & Wei Li & Xin-rong Xiao, 2015. "Optimization on Black Box Function Optimization Problem," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-10, October.
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    2. 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.
    3. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.
    4. Jun Liu & Yu Qian & Yuanjun Yang & Zhidan Yang, 2022. "Can Artificial Intelligence Improve the Energy Efficiency of Manufacturing Companies? Evidence from China," IJERPH, MDPI, vol. 19(4), pages 1-18, February.

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