Artificial intelligence in talent acquisition: a multiple case study on multi-national corporations

Abstract

Purpose

The aim of this paper is to explore how multi-national corporations (MNCs) can effectively adopt artificial intelligence (AI) into their talent acquisition (TA) practices. While the potential of AI to address emerging challenges, such as talent shortages and applicant surges in specific regions, has been anecdotally highlighted, there is limited empirical evidence regarding its effective deployment and adoption in TA. As a result, this paper endeavors to develop a theoretical model that delineates the motives, barriers, procedural steps and critical factors that can aid in the effective adoption of AI in TA within MNCs.

Design/methodology/approach

Given the scant empirical literature on our research objective, we utilized a qualitative methodology, encompassing a multiple-case study (consisting of 19 cases across seven industries) and a grounded theory approach.

Findings

Our proposed framework, termed the Framework on Effective Adoption of AI in TA, contextualizes the motives, barriers, procedural steps and critical success factors essential for the effective adoption of AI in TA.

Research limitations/ implications

This paper contributes to literature on effective adoption of AI in TA and adoption theory.

Practical implications

Additionally, it provides guidance to TA managers seeking effective AI implementation and adoption strategies, especially in the face of emerging challenges.

Originality/value

To the best of the authors' knowledge, this study is unparalleled, being both grounded in theory and based on an expansive dataset that spans firms from various regions and industries. The research delves deeply into corporations' underlying motives and processes concerning the effective adoption of AI in TA.

Citation

Roppelt, J.S., Greimel, N.S., Kanbach, D.K., Stubner, S. and Maran, T.K. (2024), "Artificial intelligence in talent acquisition: a multiple case study on multi-national corporations", Management Decision, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MD-07-2023-1194