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

Julia Stefanie Roppelt · Nina Sophie Greimel · Dominik Kurt Kanbach · Stephan Stubner · Thomas K. Maran

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AI and HR Technologies Human Resource and Talent Management Employer Branding and e-HRM

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.

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References

2007Theory Building From Cases: Opportunities And Challenges 2010Qualitative Quality: Eight “Big-Tent” Criteria for Excellent Qualitative Research 2007Methodological fit in management field research 2004Review: The Resource-Based View and Information Systems Research: Review, Extension, and Suggestions For Future Research1 2016Unified Theory of Acceptance and Use of Technology: A Synthesis and the Road Ahead 1993Organizational Learning: A Review of Some Literatures 2019Artificial Intelligence in Human Resources Management: Challenges and a Path Forward 2017Finding Theory–Method Fit: A Comparison of Three Qualitative Approaches to Theory Building

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