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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
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    Citations

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    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. Nadeem, Kashif & Wong, Sut I. & Za, Stefano & Venditti, Michelina, 2024. "Digital transformation and industry 4.0 employees: Empirical evidence from top digital nations," Technology in Society, Elsevier, vol. 76(C).
    3. 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).
    4. 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).
    5. Ertiö, Titiana & Eriksson, Taina & Rowan, Wendy & McCarthy, Stephen, 2024. "The role of digital leaders’ emotional intelligence in mitigating employee technostress," Business Horizons, Elsevier, vol. 67(4), pages 399-409.
    6. 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.
    7. Yu, Yubing & Xu, Jiawei & Zhang, Justin Z. & Liu, Yulong (David) & Kamal, Muhammad Mustafa & Cao, Yanhong, 2024. "Unleashing the power of AI in manufacturing: Enhancing resilience and performance through cognitive insights, process automation, and cognitive engagement," International Journal of Production Economics, Elsevier, vol. 270(C).
    8. Ali, Omar & Murray, Peter A. & Momin, Mujtaba & Dwivedi, Yogesh K. & Malik, Tegwen, 2024. "The effects of artificial intelligence applications in educational settings: Challenges and strategies," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    9. Deepa, R. & Sekar, Srinivasan & Malik, Ashish & Kumar, Jitender & Attri, Rekha, 2024. "Impact of AI-focussed technologies on social and technical competencies for HR managers – A systematic review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 202(C).
    10. Li, Yunjian & Song, Yixiao & Sun, Yanming & Zeng, Mingzhuo, 2024. "When do employees learn from artificial intelligence? The moderating effects of perceived enjoyment and task-related complexity," Technology in Society, Elsevier, vol. 77(C).
    11. Lee, Jaehyun & Lim, Jihye & Hwang, Junseok & Lee, Junmin, 2024. "How workers let artificial intelligence recruit and dismiss?," 24th ITS Biennial Conference, Seoul 2024. New bottles for new wine: digital transformation demands new policies and strategies 302513, International Telecommunications Society (ITS).
    12. Araz Zirar, 2023. "Can artificial intelligence’s limitations drive innovative work behaviour?," Review of Managerial Science, Springer, vol. 17(6), pages 2005-2034, August.

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