The Association between the Effectiveness of Human Resource Management Functions and the Use of Artificial Intelligence

Abstract

The main purpose of this study is to investigate the association between the effectiveness of human resource management (HRM) functions and the use of artificial intelligence (AI). The HRM functions included in this research are recruitment and selection, people analytics, and talent acquisition. A quantitative research design was used in this study. A questionnaire containing 30 questions was prepared and submitted to employees and managers working in companies in three major cities (Riyadh, Jeddah, and Dammam) in Saudi Arabia. Data were collected using the random sampling method. The study used SPSS software to analyse data collected from 50 participants. The findings reveal that there was a statistically significant association between the effectiveness of HRM functions, including recruitment and selection, people analytics, and talent acquisition, and the use of AI. In addition, the correlations between the effectiveness of HRM functions and the use of AI were moderate and strong. The contribution of this study is to enhance the literature on HRM functions and AI. In addition, the study reveals that HRM functions can operate effectively using appropriate AI programs.

Unlocking the value of artificial intelligence in human resource management through AI capability framework

“…Case in point, data stored in cloud platforms like HRIS (HR Information Systems) are composed of information on employee's demographic information (employment history, skills and competencies, formal educational qualifications and demographic information) alongside softer performance data that might be collected at appraisals and performance reviews (Angrave et al, 2016). In this context, AI-driven HR analytics has emerged as a popular research area within HRM (Baakeel, 2020), leveraging datasets stored in HRIS. It allows to redefine the way companies will manage their workforce (Giermindl et al, 2021), particularly to have a proficient workforce (i.e., suitable skills, expertise and experience) required to succeed in the organizations (Singh and Malhotra, 2020;Sivathanu and Pillai, 2018).…”

Trends in the thematic landscape of HR analytics research: a structural topic modeling approach

“…According to Tambe et al (2019), using AI enables a number of predictive tasks for hiring, choosing, onboarding, training, performance management, promotion, keeping employees and providing rewards to them. Baakeel (2020) specifically looked at the use of AI in recruiting and saw the possibility of speedy resume screening, prompt and automatic responses to candidate inquiries and virtual recruitment activities. Yang et al (2017) utilized machine learning to develop a precise and cost-efficient job recommendation system.…”

HRA adoption via organizational analytics maturity: examining the role of institutional theory, resource-based view and diffusion of innovation

“…Peisl and Edlmann (2020) investigated the factors related to the acceptance and use of Predictive HRA and concluded that the attitude of executives is the most significant one. Meanwhile, Singh and Malhotra (2020) and Baakeel (2020) found that the technological background of the organization is the main factor that impacts implementation of innovations in the HRD. Finally, McCartney et al (2020) emphasized consulting, technical expertise, data analysis skills, human capital and business intellectual capital as crucial for HRA adoption.…”