AI-augmented HRM:Antecedents, assimilation and multilevel consequences

Abstract

The current literature on the use of disruptive innovative technologies, such as artificial intelligence (AI) for human resource management (HRM) function, lacks a theoretical basis for understanding. Further, the adoption and implementation of AI-augmented HRM, which holds promise for delivering several operational, relational and transformational benefits, is at best patchy and incomplete. Integrating the technology, organisation and people (TOP) framework with core elements of the theory of innovation assimilation and its impact on a range of AI-Augmented HRM outcomes, or what we refer to as (HRM(AI)), this paper develops a coherent and integrated theoretical framework of HRM(AI) assimilation. Such a framework is timely as several post-adoption challenges, such as the dark side of processual factors in innovation assimilation and system-level factors, which, if unattended, can lead to the opacity of AI applications, thereby affecting the success of any HRM(AI). Our model proposes several testable future research propositions for advancing scholarship in this area. We conclude with implications for theory and practice.

Publication DOI: https://doi.org/10.1016/j.hrmr.2021.100860
Divisions: College of Business and Social Sciences > Aston Business School > Work & Organisational Psychology
College of Business and Social Sciences > Aston Business School > Aston India Foundation for Applied Research
College of Business and Social Sciences > Aston Business School
Additional Information: © 2021, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: Technology-driven HRM,AI-adoption in HRM,AI-augmented HRM,Processual factors
Full Text Link:
Related URLs: https://www.sci ... 0395?via%3Dihub (Publisher URL)
PURE Output Type: Article
Published Date: 2021-09-15
Published Online Date: 2021-09-15
Accepted Date: 2021-09-09
Authors: Prikshat, Verma
Malik, Ashish
Budhwar, Pawan (ORCID Profile 0000-0001-8915-6172)

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Version: Accepted Version

Access Restriction: Restricted to Repository staff only until 15 September 2023.

License: Creative Commons Attribution Non-commercial No Derivatives


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