IDEAS home Printed from https://ideas.repec.org/a/eme/ijmpps/ijm-01-2021-0052.html
   My bibliography  Save this article

Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies

Author

Listed:
  • Ahmad Arslan
  • Cary Cooper
  • Zaheer Khan
  • Ismail Golgeci
  • Imran Ali

Abstract

Purpose - This paper aims to specifically focus on the challenges that human resource management (HRM) leaders and departments in contemporary organisations face due to close interaction between artificial intelligence (AI) (primarily robots) and human workers especially at the team level. It further discusses important potential strategies, which can be useful to overcome these challenges based on a conceptual review of extant research. Design/methodology/approach - The current paper undertakes a conceptual work where multiple streams of literature are integrated to present a rather holistic yet critical overview of the relationship between AI (particularly robots) and HRM in contemporary organisations. Findings - We highlight that interaction and collaboration between human workers and robots is visible in a range of industries and organisational functions, where both are working as team members. This gives rise to unique challenges for HRM function in contemporary organisations where they need to address workers' fear of working with AI, especially in relation to future job loss and difficult dynamics associated with building trust between human workers and AI-enabled robots as team members. Along with these, human workers' task fulfilment expectations with their AI-enabled robot colleagues need to be carefully communicated and managed by HRM staff to maintain the collaborative spirit, as well as future performance evaluations of employees. The authors found that organisational support mechanisms such as facilitating environment, training opportunities and ensuring a viable technological competence level before organising human workers in teams with robots are important. Finally, we found that one of the toughest challenges for HRM relates to performance evaluation in teams where both humans and AI (including robots) work side by side. We referred to the lack of existing frameworks to guide HRM managers in this concern and stressed the possibility of taking insights from the computer gaming literature, where performance evaluation models have been developed to analyse humans and AI interactions while keeping the context and limitations of both in view. Originality/value - Our paper is one of the few studies that go beyond a rather general or functional analysis of AI in the HRM context. It specifically focusses on the teamwork dimension, where human workers and AI-powered machines (robots) work together and offer insights and suggestions for such teams' smooth functioning.

Suggested Citation

  • Ahmad Arslan & Cary Cooper & Zaheer Khan & Ismail Golgeci & Imran Ali, 2021. "Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(1), pages 75-88, July.
  • Handle: RePEc:eme:ijmpps:ijm-01-2021-0052
    DOI: 10.1108/IJM-01-2021-0052
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/IJM-01-2021-0052/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no

    File URL: https://www.emerald.com/insight/content/doi/10.1108/IJM-01-2021-0052/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: no

    File URL: https://libkey.io/10.1108/IJM-01-2021-0052?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
    ---><---

    Citations

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


    Cited by:

    1. Prikshat, Verma & Islam, Mohammad & Patel, Parth & Malik, Ashish & Budhwar, Pawan & Gupta, Suraksha, 2023. "AI-Augmented HRM: Literature review and a proposed multilevel framework for future research," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    2. Lexie Lan Huang & Rocky Peng Chen & Kimmy Wa Chan, 2024. "Pairing up with anthropomorphized artificial agents: Leveraging employee creativity in service encounters," Journal of the Academy of Marketing Science, Springer, vol. 52(4), pages 955-975, July.
    3. Zirar, Araz & Ali, Syed Imran & Islam, Nazrul, 2023. "Worker and workplace Artificial Intelligence (AI) coexistence: Emerging themes and research agenda," Technovation, Elsevier, vol. 124(C).
    4. Tursunbayeva, Aizhan & Chalutz-Ben Gal, Hila, 2024. "Adoption of artificial intelligence: A TOP framework-based checklist for digital leaders," Business Horizons, Elsevier, vol. 67(4), pages 357-368.

    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:eme:ijmpps:ijm-01-2021-0052. 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.

    We have no bibliographic references for this item. You can help adding them by using 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: Emerald Support (email available below). General contact details of provider: .

    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.