IDEAS home Printed from https://ideas.repec.org/a/eme/ijmpps/ijm-03-2021-0173.html
   My bibliography  Save this article

Impact of artificial intelligence on employees working in industry 4.0 led organizations

Author

Listed:
  • Nishtha Malik
  • Shalini Nath Tripathi
  • Arpan Kumar Kar
  • Shivam Gupta

Abstract

Purpose - This study attempts to develop a practical understanding of the positive and negative employee experiences due to artificial intelligence (AI) adoption and the creation of technostress. It unravels the human resource development-related challenges with the onset of Industry 4.0. Design/methodology/approach - Semi-structured interviews were conducted with 32 professionals with average work experience of 7.6 years and working across nine industries, and the transcripts were analyzed using NVivo. Findings - The findings establish prominent adverse impacts of the adoption of AI, namely, information security, data privacy, drastic changes resulting from digital transformations and job risk and insecurity brewing in the employee psyche. This is followed by a hierarchy of factors comprising the positive impacts, namely, work-related flexibility and autonomy, creativity and innovation and overall enhancement in job performance. Further factors contributing to technostress (among employees): work overload, job insecurity and complexity were identified. Practical implications - The emerging knowledge economy and technological interventions are changing the existing job profiles, hence the need for different skillsets and technological competencies. The organizations thus need to deploy strategic manpower development measures involving up-gradation of skills and knowledge management. Inculcating requisite skills requires well-designed training programs using specialized tools and virtual reality (VR). In addition, employees need to be supported in their evolving socio-technical relationships, for managing both positive and negative outcomes. Originality/value - This research makes the unique contribution of establishing a qualitative hierarchy of prominent factors constituting unintended consequences, positive impacts and technostress creators (among employees) of AI deployment in organizational processes.

Suggested Citation

  • Nishtha Malik & Shalini Nath Tripathi & Arpan Kumar Kar & Shivam Gupta, 2021. "Impact of artificial intelligence on employees working in industry 4.0 led organizations," International Journal of Manpower, Emerald Group Publishing Limited, vol. 43(2), pages 334-354, June.
  • Handle: RePEc:eme:ijmpps:ijm-03-2021-0173
    DOI: 10.1108/IJM-03-2021-0173
    as

    Download full text from publisher

    File URL: https://www.emerald.com/insight/content/doi/10.1108/IJM-03-2021-0173/full/html?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://www.emerald.com/insight/content/doi/10.1108/IJM-03-2021-0173/full/pdf?utm_source=repec&utm_medium=feed&utm_campaign=repec
    Download Restriction: Access to full text is restricted to subscribers

    File URL: https://libkey.io/10.1108/IJM-03-2021-0173?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.

    Citations

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


    Cited by:

    1. Nikola Soukupová, 2022. "Stress Management in Small and Medium-sized Enterprises," Economics Working Papers 2022-05, University of South Bohemia in Ceske Budejovice, Faculty of Economics.
    2. Malik, Nishtha & Kar, Arpan Kumar & Tripathi, Shalini Nath & Gupta, Shivam, 2023. "Exploring the impact of fairness of social bots on user experience," Technological Forecasting and Social Change, Elsevier, vol. 197(C).
    3. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
    4. Shivam Gupta & Sachin Modgil & Ajay Kumar & Uthayasankar Sivarajah & Zahir Irani, 2022. "Artificial intelligence and cloud-based Collaborative Platforms for Managing Disaster, extreme weather and emergency operations," Post-Print hal-04325638, HAL.
    5. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.

    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-03-2021-0173. 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.