AI in talent acquisition: a review of AI-applications used in recruitment and selection. Issue 5 (9th October 2019)

• Record Type: Journal Article
• Authors: Albert, Edward Tristram
• Is Part Of: Strategic HR review. Volume 18:Issue 5(2019)
• Page Start: 215
• Page End: 221
• Publication Date: 2019-10-09
• Subjects: Big data -- Human resources -- Artificial intelligence; Personnel management -- Periodicals; 658.305
• DOI: 10.1108/SHR-04-2019-0024
• Languages: English
• ISSNs: 1475-4398

Abstract

Abstract : Purpose: The purpose of this study is to explore the current use of artificial intelligence (AI) in the recruitment and selection of candidates. More specifically, this research investigates the level, rate and potential adoption areas for AI-tools across the hiring process. Design/methodology/approach: To fulfill that purpose, a two-step approach was adopted. First, the literature was extensively reviewed to identify potential AI-application areas supporting the recruitment and selection (R&S) process. Second, primary research was carried out in the form of semi-structured thematic interviews with different types of R&S specialists including HR managers, consultants and academics to evaluate how much of the AI-applications areas identified in the literature review are being used in practice. Findings: This study presents a multitude of findings. First, it identifies 11 areas across the R&S Process where AI-applications can be applied. However, practitioners currently seem to rely mostly on three: chatbots, screening software and task automation tools. Second, most companies adopting these AI-tools tend to be larger, tech-focussed and/or innovative firms. Finally, despite the exponential rate of AI-adoption, companies have yet to reach an inflection point as they currently show reluctance to invest in that technology for R&S. Research limitations/implications: Due to the qualitative and exploratory nature behind the research, this study displays a significant amount of subjectivity, and therefore, lacks generalisability. Despite this limitation, this study opens the door to many opportunities for academic research, both qualitative and quantitative. Originality/value: This paper addresses the huge research gap surrounding AI in R&S, pertaining specifically to the scarcity and poor quality of the current academic literature. Furthermore, this research provides a comprehensive overview of the state of AI in R&S, which will be helpful for academics and practitioners looking to rapidly gain a holistic understanding of AI in R&S.