When Technology Meets People

Business
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When Technology Meets People: The Interplay of Artificial Intelligence and Human Resource Management

The article is written by researchers, including Yusra Qamar, Rakesh Kumar Agrawal, Taab Ahmad Samad, and Charbel Jose Chiappetta Jabbour. The author notes that the application of AI in HRM continues to grow at a fast rate, yet researchers have not yet sufficiently explored the area. Therefore, the current study seeks to review existing literature to provide a solid basis for future research. Three research questions would aid the researcher in living up to the purpose of the study. The first question queries the profile of the 'state-of-the-art' research around the topic, as the second one seeks to know the particular HRM functions in which AI has found extensive application and the potential outcome. The last question concerns ways in which HRM can benefit from AI in the future as the former continues to advance rapidly.

Taking the form of a systematic literature review (SLR), the study employs an extensive online search and evaluation to select 59 journals for subsequent analysis and review. From the search, only journal articles published by July 2020 or earlier, relevant to the topic of study and appearing in the English language, were included. Any conference proceedings, magazine articles, and book chapters, among other forms of literature, were excluded. Qamar et al. (2021) then applied structural concept and content analysis to synthesise the results.  

The study found that almost all journal articles under review stated some application of AI in HRM. Among the HRM areas in which AI found application include; staffing, for example, during invitation and selection of employees; compensation especially to design a robust and inclusive remuneration system; and training and capacity development mostly during career matching. Other less covered applications include decision-making, assessment and improvement of employee emotional engagement, and development of suggestion systems. On the same note, common AI systems predominating the HRM sector include data mining, fuzzy logic, expert systems (ES), artificial neural networks (ANN), genetic algorithm (GA), and machine learning.

Nonetheless, Qamar et al. (2021) noted particular ethical concerns surrounding the application of AI in HRM. Suffice is to say that the ethical concerns, which will be outlined below, point to regulatory and institutional elements that must be considered to protect human interests while supporting the advancement of AI. According to Qamar et al. (2021), applying AI in various HRM functions may be exposed to biases and unfairness. For instance, when using expert systems to select job seekers. In addition, data mining and other forms of AI may lead to a breach of privacy. Other ethical issues revolve around transparency and data manipulation. While the prevalence of AI application in HRM functions seems inevitable, there is a need to consider these ethical concerns to protect human interests and promote the uptake of these technologies. Otherwise, the spread of relevant technology may face resistance which would cripple their efficiency.    

The author suggests that future research should focus on exploring the application of AI in HRM from angles like employee perceptions and possible misuses, management of teams, examining return on investment, and leadership perspectives. 

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