When artificial intelligence is perceived as a threat: unraveling its paradoxical impact on knowledge hiding in the workplace

心理学 调解 适度 社会心理学 验证性因素分析 自举(财务) 情商 调解 焦虑 结构方程建模 应用心理学 知识管理 常用方法方差 组织行为学 工作行为 倦怠 工作(物理) 实证研究 心理干预 组织承诺 激情 人力资源管理 感知组织支持
作者
Limin Lin,Kuoyi Lin,Qian Qian Chen,Meilian Liu
出处
期刊:Journal of Knowledge Management [Emerald Publishing Limited]
卷期号:: 1-18 被引量:2
标识
DOI:10.1108/jkm-02-2025-0164
摘要

Purpose Drawing upon conservation of resources (COR) and affective events theories, this study aims to investigate the paradoxical impact of the threat of technological disruptions (TTD) on employee knowledge hiding behavior (KHB). It tests the mediating roles of workplace anxiety and work passion and the moderating role of organizational artificial intelligence (AI) readiness to uncover the paradoxical emotional mechanisms underlying this relationship. Design/methodology/approach Data were collected from 496 employees working in AI-vulnerable industries. The data were analyzed using Statistical Product and Service Solutions (SPSS) 23.0 for reliability and descriptive statistics and Mplus 8.3 for confirmatory factor analysis and hypothesis testing. Bootstrapping was used for mediation and moderation analyses. Findings Perceived technological threat is positively associated with KHB. Workplace anxiety and work passion mediate this relationship. Workplace anxiety and obsessive passion increase knowledge hiding, whereas harmonious passion reduces it. Moreover, organizational AI readiness positively moderates the relationship between TTD and workplace anxiety. Practical implications The findings provide practical insights for managers to design interventions that reduce employees’ technological anxiety and foster work passion, thereby mitigating KHBs amid digital transformation. Originality/value Despite increasing interest in AI’s organizational implications, limited empirical research has investigated how perceived technological threats shape employees’ knowledge behaviors. This study develops a framework based on COR and affective events theories that reveals the dual emotional mechanisms and boundary conditions underlying knowledge hiding in AI-driven workplaces, advancing theoretical understanding of knowledge management behaviors under technological change.
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