Employee experience –the missing link for engaging employees: Insights from an MNE's AI-based HR ecosystem


Abstract: Analyzing multiple data sources from a global information technology (IT) consulting multinational enterprise (MNE), this research unpacks the configuration of a digitalized HR ecosystem of artificial intelligence(AI)‐assisted human resource management (HRM) applications and HR platforms. This study develops a novel theoretical framework mapping the nature and purpose of a digitalized AI‐assisted HR ecosystem for delivering exceptional employee experience (EX), an antecedent to employee engagement (EE). Employing the theoretical lenses of EX, EE, AI‐mediated social exchange, and engagement platforms, this study's overarching aim of this article is to establish how AI‐assisted HRM fits into an organization's ecosystem and, second, how it impacts EX and EE. Our findings show that AI‐assisted applications for HRM enhance EX and, thus, EE. We also see increases in employee productivity and HR function's effectiveness. Implications for research and practice are also discussed.

Publication DOI: https://doi.org/10.1002/hrm.22133
Divisions: College of Business and Social Sciences > Aston Business School > Work & Organisational Psychology
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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: © 2022 The Authors. Human Resource Management published by Wiley Periodicals LLC. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
Uncontrolled Keywords: HR ecosystem,India,artificial intelligence applications,employee experience,engagement,engagement platforms,hyper-personalization,individualization,Applied Psychology,Strategy and Management,Organizational Behavior and Human Resource Management,Management of Technology and Innovation
Publication ISSN: 1099-050X
Full Text Link:
Related URLs: https://onlinel ... .1002/hrm.22133 (Publisher URL)
http://www.scop ... tnerID=8YFLogxK (Scopus URL)
PURE Output Type: Article
Published Date: 2023-01-01
Published Online Date: 2022-07-20
Accepted Date: 2022-06-09
Submitted Date: 2021-01-01
Authors: Malik, Ashish
Budhwar, Pawan (ORCID Profile 0000-0001-8915-6172)
Mohan, Hrishi
N. R., Srikanth



Version: Published Version

License: Creative Commons Attribution

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