University of Oulu

Arslan, A., Cooper, C., Khan, Z., Golgeci, I. and Ali, I. (2022), "Artificial intelligence and human workers interaction at team level: a conceptual assessment of the challenges and potential HRM strategies", International Journal of Manpower, Vol. 43 No. 1, pp. 75-88.

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

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Author: Arslan, Ahmad1; Cooper, Cary2; Khan, Zaheer3;
Organizations: 1Department of Marketing, Management and IB, University of Oulu, Oulu, Finland
2Alliance Manchester Business School, The University of Manchester, Manchester,UK
3Business School, University of Aberdeen, Aberdeen, UK
4Department of Business Development and Technology, Aarhus University, Herning, Denmark
5School of Business and Law, Central Queensland University, Cairns, Australia
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 0.2 MB)
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Language: English
Published: Emerald, 2022
Publish Date: 2021-08-17


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.

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Series: International journal of manpower
ISSN: 0143-7720
ISSN-E: 1758-6577
ISSN-L: 0143-7720
Volume: 43
Issue: 1
Pages: 75 - 88
DOI: 10.1108/IJM-01-2021-0052
Type of Publication: A1 Journal article – refereed
Field of Science: 512 Business and management
Copyright information: © 2021, Ahmad Arslan, Cary Cooper, Zaheer Khan, Ismail Golgeci and Imran Ali. Publisher: Emerald Publishing Limited. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at