Unpacking human-AI interaction: Exploring unintended consequences on employee Well-being in entrepreneurial firms through an in-depth analysis
Year: 2025
Type: article
Abstract: This study explores the influence of AI-based HRM systems on employee well-being in seasoned entrepreneurial firms through a comparative longitudinal case study of six high-technology ventures. The findings reveal four main shadow experiences associated with AI-based HRM systems, including the erosion of interpersonal autonomy, surveillance-induced precarity, algorithmic bias dilemma, and personalized discontentment. These experiences contribute to three distinct categories of well-being shadows: psychological alienation, physical adaptive overload, and social marginalization. This study clarifies the complex mechanisms linking shadow experiences to well-being outcomes and identifies enablers that can mitigate adverse effects, such as agility and personal growth, streamlined efficiency and harmony, and resource empowerment and engagement. It also proposes actionable pathways for employees to address and overcome these shadows, including deepened introspection, empowered inner power, and refined resourcefulness. The findings provide novel insights into the dual-edged nature of human-AI interaction and offer insights for promoting sustainable well-being in the evolving workplace environment.
Source: Journal of Business Research
Authors: Jianwen Zheng, Justin Zuopeng Zhang, Muhammad Mustafa Kamal, Xiaoyang Liang, Ebtesam Abdullah Alzeiby
Language: English
Topic: AI and HR Technologies
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,440
OpenAlex ID: W4410079225