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.

Finding a fit between CXO’s experience and AI usage in CXO decision-making: evidence from knowledge-intensive professional service firms

“…While there are pieces of evidence of the benefits of its use in a range of functional areas, humans often are not willing to accept machines or AI solutions in their lives (Malik et al. , 2022a, b, c; Sinha et al.…”

“…Nguyen and Malik (2022 a, b) and Prikshat et al (2023) provide some guidance on the importance of the technology's usability and the AI applications' service quality and design features as critical in enhancing the effectiveness of decision-making. AI-augmented applications help improve decision-making for employee experience and engagement (Malik et al, 2022a).…”

“…, 2023), such as recruitment and selection and talent management (Malik et al. , 2021, 2022a, b, c). There have been significant breakthroughs in AI apps’ processing power, exponential increases in data availability and new ML algorithms (Haesevoets et al.…”

“…AI adoption in the workplace often deploys algorithmic data analytics and automation of tasks and processes using machine learning (ML) to improve decision-making with little or no human intervention (Bader and Kaiser, 2019;Dear, 2019). Latest developments in AI are revolutionizing managerial functions, such as auditing services (Tiron-Tudor and Deliu, 2022), portfolio management (Huang and Rust, 2018), automated trade in financial markets (Ivanov, 2022) and aspects of people management (Budhwar et al, 2022;Malik et al, 2023), such as recruitment and selection and talent management (Malik et al, 2021(Malik et al, , 2022a. There have been significant breakthroughs in AI apps' processing power, exponential increases in data availability and new ML algorithms (Haesevoets et al, 2021).…”

Forecasting and Coupled Coordination Analysis of Supply and Demand for Sustainable Talent in Chinese Agriculture

“…Researchers have developed a rich body of information on issues related to the field of talent management, providing important guidance for achieving talent alignment. Positive subjective employee perceptions [30,31] and an appropriate corporate development climate [32] contribute to an increase in talent supply; industry and social trends provide important guides for analyzing changes in talent demand [33,34]. Researchers have directly investigated the talent coordination management problem, such as Erin et al, who developed a theoretical analysis framework for the talent skill mismatch problem in supply chain management [5].…”