Towards effective adoption of artificial intelligence in talent acquisition: A mixed method study

Abstract

Artificial intelligence (AI) emerges as a promising technology to address burgeoning challenges resulting from shifting demographics, coupled with a shortage of qualified personnel. Thus, the adoption of AI creates especially interest within the talent acquisition (TA) domain to realize anticipated efficiency gains. However, evidence suggests that AI adoption may foster the emergence of harmful forms of practices (HFP) within TA practices. Despite the importance, respective empirical studies collecting data to generate insights remain sparse. Thus, the aim of this study is to investigate HFP and underlying drivers through a mixed-method approach. At the first stage, we conducted in-depth interviews with 42 TA experts. The resulting insights informed the development of the 'Adoption of AI in TA: Framework on Negative Consequences.' This model suggests that a confluence of technological, individual, and organizational factors can result in the emergence of HFP post-AI adoption. Such potential HFP include biased decision-making, data privacy violations, and efficiency reduction. Then, we validated our qualitative findings and confirmed our hypotheses by employing a quantitative, survey-based approach with 303 valid study participants. By shedding light on potential HFP through AI adoption in TA and respective catalysts, our research empowers both information technology and TA professionals to proactively engage in mitigation strategies. In this vein, they may successfully navigate the complex landscape of AI adoption. Hence, this study adds to research on effective AI adoption in TA.